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Transactions on Machine Learning Research, Volume 2023
Volume 2023, 2023
- Robert Schmier, Ullrich Köthe, Christoph-Nikolas Straehle:
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data. - Newton Mwai Kinyanjui, Emil Carlsson, Fredrik D. Johansson:
Fast Treatment Personalization with Latent Bandits in Fixed-Confidence Pure Exploration. - Giannis Daras, Mauricio Delbracio, Hossein Talebi, Alex Dimakis, Peyman Milanfar:
Soft Diffusion: Score Matching with General Corruptions. - Maxime Haddouche, Benjamin Guedj:
PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales. - Qingfeng Lan, Yangchen Pan
, Jun Luo, A. Rupam Mahmood:
Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation. - Manoj Kumar, Mostafa Dehghani, Neil Houlsby:
Dual PatchNorm. - Tian Yun, Usha Bhalla, Ellie Pavlick, Chen Sun:
Do Vision-Language Pretrained Models Learn Composable Primitive Concepts? - Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar:
A Revenue Function for Comparison-Based Hierarchical Clustering. - Remo Sasso, Matthia Sabatelli, Marco A. Wiering:
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning. - Diego A. Velázquez, Pau Rodríguez, Alexandre Lacoste, Issam H. Laradji, F. Xavier Roca, Jordi Gonzàlez:
Explaining Visual Counterfactual Explainers. - Guillaume Morel, Lucas Drumetz, Simon Benaïchouche, Nicolas Courty, François Rousseau:
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows. - Akshaj Kumar Veldanda, Ivan Brugere, Jiahao Chen, Sanghamitra Dutta, Alan Mishler, Siddharth Garg:
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss. - Fahad Sarfraz, Elahe Arani, Bahram Zonooz:
A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning. - Barna Pásztor, Andreas Krause, Ilija Bogunovic:
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning. - Afonso Eduardo, Michael U. Gutmann:
Bayesian Optimization with Informative Covariance. - Frederik Harder, Milad Jalali, Danica J. Sutherland, Mijung Park:
Pre-trained Perceptual Features Improve Differentially Private Image Generation. - Fabian Schaipp, Robert M. Gower, Michael Ulbrich:
A Stochastic Proximal Polyak Step Size. - Xinran Zhu, Leo Huang, Eric Hans Lee, Cameron Alexander Ibrahim, David Bindel:
Bayesian Transformed Gaussian Processes. - Jacobie Mouton, Rodney Stephen Kroon:
Integrating Bayesian Network Structure into Residual Flows and Variational Autoencoders. - Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jérôme Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti:
Data Models for Dataset Drift Controls in Machine Learning With Optical Images. - Rishi Sonthalia, Raj Rao Nadakuditi:
Training Data Size Induced Double Descent For Denoising Feedforward Neural Networks and the Role of Training Noise. - Ambar Pal, Jeremias Sulam:
Understanding Noise-Augmented Training for Randomized Smoothing. - Yuhang Li, Youngeun Kim, Hyoungseob Park, Priyadarshini Panda:
Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient. - Sami Jullien, Mozhdeh Ariannezhad, Paul Groth, Maarten de Rijke:
A Simulation Environment and Reinforcement Learning Method for Waste Reduction. - Vignesh Kothapalli:
Neural Collapse: A Review on Modelling Principles and Generalization. - Clément Lalanne, Aurélien Garivier, Rémi Gribonval:
On the Statistical Complexity of Estimation and Testing under Privacy Constraints. - Amrit Nagarajan, Anand Raghunathan:
FASTRAIN-GNN: Fast and Accurate Self-Training for Graph Neural Networks. - Zhili Feng, Ezra Winston, J. Zico Kolter:
Monotone deep Boltzmann machines. - Mohamed Abdelhack, Jiaming Zhang, Sandhya Tripathi, Bradley A. Fritz, Daniel Felsky, Michael Avidan, Yi-Xin Chen, Christopher Ryan King:
A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues. - Asher Trockman, J. Zico Kolter:
Patches Are All You Need? - Jiaqi Ma, Ziqiao Ma, Joyce Chai, Qiaozhu Mei:
Partition-Based Active Learning for Graph Neural Networks. - Dongyue Li, Huy L. Nguyen
Identification of Negative Transfers in Multitask Learning Using Surrogate Models. - Dennis Wagner, Tobias Michels, Florian C. F. Schulz, Arjun Nair, Maja Rudolph, Marius Kloft:
TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection. - Tiange Luo, Honglak Lee, Justin Johnson:
Neural Shape Compiler: A Unified Framework for Transforming between Text, Point Cloud, and Program. - Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. - Maurits J. R. Bleeker, Andrew Yates, Maarten de Rijke:
Reducing Predictive Feature Suppression in Resource-Constrained Contrastive Image-Caption Retrieval. - Zijie Li, Kazem Meidani, Amir Barati Farimani:
Transformer for Partial Differential Equations' Operator Learning. - Dennis Ulmer, Christian Hardmeier, Jes Frellsen:
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation. - Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C. Hughes:
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds. - David Kuric, Herke van Hoof:
Reusable Options through Gradient-based Meta Learning. - Oyku Deniz Kose, Yanning Shen:
Fast&Fair: Training Acceleration and Bias Mitigation for GNNs. - Haotao Wang, Junyuan Hong, Jiayu Zhou, Zhangyang Wang:
How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts. - Pascal Kilian, Sangbeak Ye, Augustin Kelava:
Mixed effects in machine learning - A flexible mixedML framework to add random effects to supervised machine learning regression. - Harsh Satija, Alessandro Lazaric, Matteo Pirotta, Joelle Pineau:
Group Fairness in Reinforcement Learning. - Josephine Maria Thomas, Alice Moallemy-Oureh, Silvia Beddar-Wiesing, Clara Holzhüter:
Graph Neural Networks Designed for Different Graph Types: A Survey. - Alina Selega, Kieran R. Campbell:
Multi-objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows. - Patrick M. Soga, David Chiang:
Bridging Graph Position Encodings for Transformers with Weighted Graph-Walking Automata. - Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan:
Machine Explanations and Human Understanding. - Alaaeldin El-Nouby, Matthew J. Muckley, Karen Ullrich, Ivan Laptev, Jakob Verbeek, Hervé Jégou:
Image Compression with Product Quantized Masked Image Modeling. - Georgios Tzannetos, Bárbara Gomes Ribeiro, Parameswaran Kamalaruban, Adish Singla:
Proximal Curriculum for Reinforcement Learning Agents. - Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
Jacobian-based Causal Discovery with Nonlinear ICA. - Matteo Gamba, Erik Englesson, Mårten Björkman, Hossein Azizpour:
Deep Double Descent via Smooth Interpolation. - Alan Q. Wang, Mert R. Sabuncu:
A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification. - Nathan J. Wispinski, Andrew Butcher, Kory Wallace Mathewson, Craig S. Chapman, Matthew M. Botvinick, Patrick M. Pilarski:
Adaptive patch foraging in deep reinforcement learning agents. - Firat Ozdemir, Berkan Lafci, Xosé-Luís Dean-Ben, Daniel Razansky, Fernando Pérez-Cruz:
OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing. - Magnus Ross, Markus Heinonen:
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes. - Donlapark Ponnoprat:
Dirichlet Mechanism for Differentially Private KL Divergence Minimization. - Shengyuan Hu, Steven Wu, Virginia Smith:
Private Multi-Task Learning: Formulation and Applications to Federated Learning. - Yiling Xie, Yiling Luo, Xiaoming Huo:
Solving a Special Type of Optimal Transport Problem by a Modified Hungarian Algorithm. - Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, Kwok-Wai Cheung, Bo Han:
KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation. - Matthew Koichi Grimes, Joseph Modayil, Piotr W. Mirowski, Dushyant Rao, Raia Hadsell:
Learning to Look by Self-Prediction. - Yi Heng Lim, Muhammad Firmansyah Kasim:
Unifying physical systems' inductive biases in neural ODE using dynamics constraints. - Damjan Kalajdzievski, Ximeng Mao, Pascal Fortier-Poisson, Guillaume Lajoie, Blake Aaron Richards:
Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer. - Axel Böhm:
Solving Nonconvex-Nonconcave Min-Max Problems exhibiting Weak Minty Solutions. - Bahjat Kawar, Roy Ganz, Michael Elad:
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance. - Farnaz Adib Yaghmaie, Hamidreza Modares:
Online Optimal Tracking of Linear Systems with Adversarial Disturbances. - Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford:
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models. - Juan Miguel Lopez Alcaraz, Nils Strodthoff:
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models. - Lucas Prieto, Jeroen den Boef, Paul Groth, Joran Cornelisse:
Parameter Efficient Node Classification on Homophilic Graphs. - David A. Klindt:
Controlling Neural Network Smoothness for Neural Algorithmic Reasoning. - Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer
MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. - Harsh Mehta, Walid Krichene, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky
Differentially Private Image Classification from Features. - Nasir Ahmad, Ellen Schrader, Marcel van Gerven:
Constrained Parameter Inference as a Principle for Learning. - Yuanqi Du, Xian Liu, Nilay Mahesh Shah, Shengchao Liu, Jieyu Zhang, Bolei Zhou:
ChemSpacE: Interpretable and Interactive Chemical Space Exploration. - Anuj Singh, Hadi Jamali Rad:
Transductive Decoupled Variational Inference for Few-Shot Classification. - Roy Ganz, Michael Elad:
BIGRoC: Boosting Image Generation via a Robust Classifier. - Zhirong Wu, Zihang Lai, Xiao Sun, Stephen Lin:
Extreme Masking for Learning Instance and Distributed Visual Representations. - Ziyi Chen, Zhengyang Hu, Qunwei Li, Zhe Wang, Yi Zhou:
A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization. - Guanlin Liu, Lifeng Lai:
Action Poisoning Attacks on Linear Contextual Bandits. - Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou:
Assisted Learning for Organizations with Limited Imbalanced Data. - Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A. Naesseth:
A Variational Perspective on Generative Flow Networks. - Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Tomas Pfister:
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. - Zhiliang Peng, Li Dong, Hangbo Bao, Furu Wei, Qixiang Ye:
A Unified View of Masked Image Modeling. - Zhangheng Li, Tianlong Chen, Linyi Li, Bo Li, Zhangyang Wang:
Can Pruning Improve Certified Robustness of Neural Networks? - Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi:
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning. - Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami:
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. - Xingran Chen, Hesam Nikpey, Jungyeol Kim, Saswati Sarkar, Shirin Saeedi Bidokhti:
Containing a spread through sequential learning: to exploit or to explore? - Ryoya Yamasaki
Optimal Threshold Labeling for Ordinal Regression Methods. - Shuo Sun, Molei Qin, Xinrun Wang, Bo An:
PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets. - Boxin Zhao, Boxiang Lyu, Mladen Kolar:
L-SVRG and L-Katyusha with Adaptive Sampling. - Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael G. Rabbat, Ari S. Morcos:
lo-fi: distributed fine-tuning without communication. - Simon Wiedemann, Daniel Hein, Steffen Udluft, Christian B. Mendl:
Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning. - Zibo Liu, Parshin Shojaee, Chandan K. Reddy:
Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting. - Matthew James Vowels, Sina Akbari, Necati Cihan Camgöz, Richard Bowden:
A Free Lunch with Influence Functions? An Empirical Evaluation of Influence Functions for Average Treatment Effect Estimation. - Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann:
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. - Or Feldman, Amit Boyarski, Shai Feldman, Dani Kogan, Avi Mendelson, Chaim Baskin:
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings. - Yuntao Du, Juan Jiang, Hongtao Luo, Haiyang Yang, Mingcai Chen, Chongjun Wang:
Bidirectional View based Consistency Regularization for Semi-Supervised Domain Adaptation. - Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li, Yong Lin, Xiao Zhou, Tong Zhang:
Black-Box Prompt Learning for Pre-trained Language Models. - Harsh Mehta, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky
Towards Large Scale Transfer Learning for Differentially Private Image Classification. - Joy Hsu, Jiayuan Mao, Jiajun Wu:
DisCo: Improving Compositional Generalization in Visual Reasoning through Distribution Coverage. - Bartlomiej Polaczyk, Jacek Cyranka:
Improved Overparametrization Bounds for Global Convergence of SGD for Shallow Neural Networks. - Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri:
Probing Predictions on OOD Images via Nearest Categories. - Marissa Catherine Connor, Kion Fallah, Christopher John Rozell
Learning Identity-Preserving Transformations on Data Manifolds. - Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid, Aaron Walsman, Aniruddha Kembhavi, Ali Farhadi:
FLUID: A Unified Evaluation Framework for Flexible Sequential Data. - Leo Kozachkov, Patrick M. Wensing, Jean-Jacques E. Slotine:
Generalization as Dynamical Robustness-The Role of Riemannian Contraction in Supervised Learning. - Zhen Xu, Quanming Yao, Yong Li, Qiang Yang:
Understanding and Simplifying Architecture Search in Spatio-Temporal Graph Neural Networks. - Enayat Ullah, Harry Lang, Raman Arora, Vladimir Braverman:
Clustering using Approximate Nearest Neighbour Oracles. - Enayat Ullah, Raman Arora:
Generalization bounds for Kernel Canonical Correlation Analysis. - Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao:
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. - Yixuan Su, Nigel Collier:
Contrastive Search Is What You Need For Neural Text Generation. - Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor:
Learning Representations for Pixel-based Control: What Matters and Why? - Felix Dangel, Lukas Tatzel, Philipp Hennig:
ViViT: Curvature Access Through The Generalized Gauss-Newton's Low-Rank Structure. - Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon:
U-Statistics for Importance-Weighted Variational Inference. - Guojun Zhang, Saber Malekmohammadi, Xi Chen, Yaoliang Yu:
Proportional Fairness in Federated Learning. - Zhonghao Zhang, Yipeng Liu, Xingyu Cao, Fei Wen, Ce Zhu:
Scalable Deep Compressive Sensing. - Tony Tohme, Dehong Liu, Kamal Youcef-Toumi:
GSR: A Generalized Symbolic Regression Approach. - Andrea Schioppa, Nal Kalchbrenner:
Stacking Diverse Architectures to Improve Machine Translation. - Zhiying Fang, Guang Cheng:
Optimal Convergence Rates of Deep Convolutional Neural Networks: Additive Ridge Functions. - Amine El Hattami, Issam H. Laradji, Stefania Raimondo, David Vázquez, Pau Rodríguez, Christopher Pal:
Workflow Discovery from Dialogues in the Low Data Regime. - Sean Gunn, Jorio Cocola, Paul Hand:
Regularized Training of Intermediate Layers for Generative Models for Inverse Problems. - Qijun Luo, Xiao Li:
Finite-Time Analysis of Decentralized Single-Timescale Actor-Critic. - Jonas Gehring, Deepak Gopinath, Jungdam Won, Andreas Krause, Gabriel Synnaeve, Nicolas Usunier:
Leveraging Demonstrations with Latent Space Priors. - Jiamin Chen, Xuhong Li, Lei Yu, Dejing Dou, Haoyi Xiong:
Beyond Intuition: Rethinking Token Attributions inside Transformers. - Vijaya Raghavan T. Ramkumar, Elahe Arani, Bahram Zonooz:
Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks. - Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola:
The Low-Rank Simplicity Bias in Deep Networks. - Saiteja Utpala, Praneeth Vepakomma, Nina Miolane:
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric. - Jing Wu, David Pichler, Daniel Marley, Naira Hovakimyan, David Wilson, Jennifer A. Hobbs:
Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis. - Salem Lahlou, Moksh Jain, Hadi Nekoei, Victor Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, Yoshua Bengio:
DEUP: Direct Epistemic Uncertainty Prediction. - Serge Assaad, Carlton Downey, Rami Al-Rfou', Nigamaa Nayakanti, Benjamin Sapp:
VN-Transformer: Rotation-Equivariant Attention for Vector Neurons. - Pradeep Kumar Jayaraman, Joseph George Lambourne, Nishkrit Desai, Karl D. D. Willis, Aditya Sanghi, Nigel J. W. Morris:
SolidGen: An Autoregressive Model for Direct B-rep Synthesis. - Jireh Huang, Qing Zhou:
Bayesian Causal Bandits with Backdoor Adjustment Prior. - Mona Buisson-Fenet, Valéry Morgenthaler, Sebastian Trimpe, Florent Di Meglio:
Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs. - Maximilian Stubbemann, Tom Hanika
Intrinsic Dimension for Large-Scale Geometric Learning. - Sander Dalm, Nasir Ahmad, Luca Ambrogioni, Marcel van Gerven:
Gradient-adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neural Networks. - Francisca Vasconcelos, Bobby He, Nalini M. Singh, Yee Whye Teh:
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography. - Diyuan Wu, Vyacheslav Kungurtsev, Marco Mondelli:
Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. - Dani Kiyasseh, Tingting Zhu, David A. Clifton:
PCPs: Patient Cardiac Prototypes to Probe AI-based Medical Diagnoses, Distill Datasets, and Retrieve Patients. - Vikranth Dwaracherla, Zheng Wen, Ian Osband, Xiuyuan Lu, Seyed Mohammad Asghari, Benjamin Van Roy:
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping. - Longhui Yu, Tianyang Hu, Lanqing Hong, Zhen Liu, Adrian Weller, Weiyang Liu:
Continual Learning by Modeling Intra-Class Variation. - Ondrej Such, René Fabricius:
Bridging performance gap between minimal and maximal SVM models. - Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer, Bernhard C. Geiger:
On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks. - Han Zhao:
Costs and Benefits of Fair Regression. - Shusheng Xu, Yancheng Liang, Yunfei Li, Simon Shaolei Du, Yi Wu:
Beyond Information Gain: An Empirical Benchmark for Low-Switching-Cost Reinforcement Learning. - Kiri L. Wagstaff, Thomas G. Dietterich:
Hidden Heterogeneity: When to Choose Similarity-Based Calibration. - Felipe A. Tobar, Elsa Cazelles, Taco de Wolff:
Computationally-efficient initialisation of GPs: The generalised variogram method. - Rahul Singh, Yongxin Chen:
Signed Graph Neural Networks: A Frequency Perspective. - Tiansheng Huang, Li Shen, Yan Sun, Weiwei Lin, Dacheng Tao:
Fusion of Global and Local Knowledge for Personalized Federated Learning. - Yiyou Sun, Yixuan Li:
OpenCon: Open-world Contrastive Learning. - Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra:
Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction. - Matteo Zecchin, Marios Kountouris, David Gesbert:
Communication-Efficient Distributionally Robust Decentralized Learning. - Amirali Aghazadeh, Nived Rajaraman, Tony Tu, Kannan Ramchandran:
Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces. - Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Jörn-Henrik Jacobsen:
Robust Hybrid Learning With Expert Augmentation. - Antti Koskela, Mikko A. Heikkilä, Antti Honkela:
Numerical Accounting in the Shuffle Model of Differential Privacy. - Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully:
Accelerated Quality-Diversity through Massive Parallelism. - Soufiane Hayou:
On the infinite-depth limit of finite-width neural networks. - Jarrod Haas, William Yolland, Bernhard Rabus:
Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks. - Zahra Atashgahi, Xuhao Zhang, Neil Kichler, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu:
Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks. - Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou, Peter Richtárik, Martin Takác:
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. - Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth:
Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation. - Ahmed Khaled, Peter Richtárik:
Better Theory for SGD in the Nonconvex World. - Daniel Rebain, Mark J. Matthews, Kwang Moo Yi, Gopal Sharma, Dmitry Lagun, Andrea Tagliasacchi:
Attention Beats Concatenation for Conditioning Neural Fields. - Charlie Nash, João Carreira, Jacob C. Walker, Iain Barr, Andrew Jaegle, Mateusz Malinowski, Peter W. Battaglia:
Transframer: Arbitrary Frame Prediction with Generative Models. - Kiarash Banihashem, Adish Singla, Goran Radanovic:
Defense Against Reward Poisoning Attacks in Reinforcement Learning. - Marine Picot, Federica Granese, Guillaume Staerman, Marco Romanelli, Francisco Messina, Pablo Piantanida, Pierre Colombo:
A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection. - Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela:
Differentially private partitioned variational inference. - Harshit Sikchi, Akanksha Saran, Wonjoon Goo, Scott Niekum:
A Ranking Game for Imitation Learning. - Maggie Makar, Alexander D'Amour:
Fairness and robustness in anti-causal prediction. - Sachin Mehta, Mohammad Rastegari:
Separable Self-attention for Mobile Vision Transformers. - Thomas Pethick, Grigorios Chrysos, Volkan Cevher:
Revisiting adversarial training for the worst-performing class. - Shagun Sodhani, Sergey Levine, Amy Zhang:
Improving Generalization with Approximate Factored Value Functions. - Atish Agarwala, Samuel Stern Schoenholz, Jeffrey Pennington, Yann N. Dauphin:
Temperature check: theory and practice for training models with softmax-cross-entropy losses. - Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard D. Bondell:
FedDAG: Federated DAG Structure Learning. - Daniel Shin, Anca D. Dragan, Daniel S. Brown:
Benchmarks and Algorithms for Offline Preference-Based Reward Learning. - David Chiang, Alexander M. Rush, Boaz Barak:
Named Tensor Notation. - Xingjian Li, Haoyi Xiong, Cheng-Zhong Xu, Dejing Dou:
SMILE: Sample-to-feature Mixup for Efficient Transfer Learning. - Valerii Likhosherstov, Anurag Arnab, Krzysztof Marcin Choromanski, Mario Lucic, Yi Tay, Mostafa Dehghani:
PolyViT: Co-training Vision Transformers on Images, Videos and Audio. - Aakarsh Malhotra, Mayank Vatsa, Richa Singh:
Dropped Scheduled Task: Mitigating Negative Transfer in Multi-task Learning using Dynamic Task Dropping. - Matthew Schlegel, Volodymyr Tkachuk, Adam M. White, Martha White:
Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning. - Mihaela Rosca, Yan Wu, Chongli Qin, Benoit Dherin:
On a continuous time model of gradient descent dynamics and instability in deep learning. - Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters:
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. - Christian Fröhlich, Robert C. Williamson:
Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity. - Muhammad Osama, Dave Zachariah, Peter Stoica, Thomas B. Schön:
Online Learning for Prediction via Covariance Fitting: Computation, Performance and Robustness. - Tianjian Huang, Shaunak Ashish Halbe, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn, Ahmad Beirami:
Robustness through Data Augmentation Loss Consistency. - Thibaut Issenhuth, Ugo Tanielian, Jérémie Mary, David Picard:
EdiBERT: a generative model for image editing. - Christoffer Löffler, Kion Fallah, Stefano Fenu, Dario Zanca, Björn M. Eskofier, Christopher John Rozell, Christopher Mutschler:
Active Learning of Ordinal Embeddings: A User Study on Football Data. - Angus Galloway, Anna Golubeva, Mahmoud Salem, Mihai Nica, Yani A. Ioannou, Graham W. Taylor:
Bounding generalization error with input compression: An empirical study with infinite-width networks. - Macheng Shen, Jonathan P. How:
Implicit Ensemble Training for Efficient and Robust Multiagent Reinforcement Learning. - Vincent Roulet, Zaïd Harchaoui:
Target Propagation via Regularized Inversion for Recurrent Neural Networks. - Yifan Chen, Tianning Xu, Dilek Hakkani-Tur, Di Jin, Yun Yang, Ruoqing Zhu:
Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks. - Jicong Fan, Lijun Ding, Chengrun Yang, Zhao Zhang, Madeleine Udell:
Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis. - Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gómez-Bombarelli, Tommi S. Jaakkola:
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations. - Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew K. Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakas, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartlomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, Cèsar Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodolà, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, Ethan J. Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, François Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-López, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Shevlin, Hinrich Schütze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Kocon, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse H. Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jörg Frohberg, Jos Rozen, José Hernández-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, Kory Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Colón, Luke Metz, Lütfi Kerem Senel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, María José Ramírez-Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Mátyás Schubert, Medina Baitemirova, Melody Arnaud, Melvin McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael I. Ivanitskiy, Michael Starritt, Michael Strube, Michal Swedrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T., Nanyun Peng, Nathan A. Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Milkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan LeBras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima (Shammie) Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven T. Piantadosi, Stuart M. Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsu Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay V. Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, Zirui Wang, Ziyi Wu:
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Md Ashiqur Rahman, Zachary E. Ross, Kamyar Azizzadenesheli:
U-NO: U-shaped Neural Operators. - Elvijs Sarkans, Sumon Ahmed, Magnus Rattray, Alexis Boukouvalas:
Modelling sequential branching dynamics with a multivariate branching Gaussian process. - Ruo Yu Tao, Adam White, Marlos C. Machado:
Agent-State Construction with Auxiliary Inputs. - Varun Raj Kompella, Thomas Walsh, Samuel Barrett, Peter R. Wurman, Peter Stone:
Event Tables for Efficient Experience Replay. - Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Justice Sefas, Yunpeng Liu, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood:
Conditional Permutation Invariant Flows. - Abhijay Ghildyal, Feng Liu:
Attacking Perceptual Similarity Metrics. - Wenbin Li, Xuesong Yang, Meihao Kong, Lei Wang, Jing Huo, Yang Gao, Jiebo Luo:
Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings. - Utku Ozbulak, Hyun Jung Lee, Beril Boga, Esla Timothy Anzaku, Ho-min Park, Arnout Van Messem, Wesley De Neve, Joris Vankerschaver:
Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training. - David A. Ehrlich, Andreas C. Schneider, Viola Priesemann, Michael Wibral, Abdullah Makkeh:
A Measure of the Complexity of Neural Representations based on Partial Information Decomposition. - Wenkai Yang, Yankai Lin, Guangxiang Zhao, Peng Li, Jie Zhou, Xu Sun:
When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning. - An Xiao, Hanting Chen, Tianyu Guo, Qinghua Zhang, Yunhe Wang:
Deep Plug-and-Play Clustering with Unknown Number of Clusters. - Kumail Alhamoud, Hasan Abed Al Kader Hammoud, Motasem Alfarra, Bernard Ghanem:
Generalizability of Adversarial Robustness Under Distribution Shifts. - Yunhao Ge, Yuecheng Li, Di Wu, Ao Xu, Adam M. Jones, Amanda Sofie Rios, Iordanis Fostiropoulos, Shixian Wen, Po-Hsuan Huang, Zachary William Murdock, Gozde Sahin, Shuo Ni, Kiran Lekkala, Sumedh Anand Sontakke, Laurent Itti:
Lightweight Learner for Shared Knowledge Lifelong Learning. - Chris Reinke, Xavier Alameda-Pineda:
Successor Feature Representations. - Florian Bordes, Randall Balestriero, Quentin Garrido, Adrien Bardes, Pascal Vincent:
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning. - Arrasy Rahman, Elliot Fosong, Ignacio Carlucho, Stefano V. Albrecht:
Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity. - Filip Hanzely, Boxin Zhao, Mladen Kolar:
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques. - Xenia Miscouridou, Samir Bhatt, George O. Mohler, Seth R. Flaxman, Swapnil Mishra:
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes. - Gideon Dresdner, Dmitrii Kochkov, Peter Christian Norgaard, Leonardo Zepeda-Núñez, Jamie A. Smith, Michael P. Brenner, Stephan Hoyer:
Learning to correct spectral methods for simulating turbulent flows. - Tomer Galanti, Liane Galanti, Ido Ben-Shaul:
Comparative Generalization Bounds for Deep Neural Networks. - Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister:
Interpretable Mixture of Experts. - Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri:
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State. - Calypso Herrera, Florian Krach, Anastasis Kratsios, Pierre Ruyssen, Josef Teichmann:
Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices. - Rohit Agarwal, Deepak K. Gupta, Alexander Horsch, Dilip K. Prasad:
Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts. - Anh Tuan Bui, Trung Le, He Zhao, Quan Hung Tran, Paul Montague, Dinh Phung:
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization. - Xiaotian Han, Zhimeng Jiang, Hongye Jin, Zirui Liu, Na Zou, Qifan Wang, Xia Hu:
Retiring ΔDP: New Distribution-Level Metrics for Demographic Parity. - Bálint Máté, François Fleuret:
Learning Interpolations between Boltzmann Densities. - Paul Pu Liang, Yiwei Lyu, Xiang Fan, Jeffrey Tsaw, Yudong Liu, Shentong Mo, Dani Yogatama, Louis-Philippe Morency, Russ Salakhutdinov:
High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning. - Wei Huang, Chunrui Liu, Yilan Chen, Richard Yi Da Xu, Miao Zhang, Tsui-Wei Weng:
Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection. - Alexander P. Wu, Thomas Markovich, Bonnie Berger, Nils Yannick Hammerla, Rohit Singh:
Causally-guided Regularization of Graph Attention Improves Generalizability. - Yutong Dai, Tianyi Chen, Guanyi Wang, Daniel P. Robinson:
An Adaptive Half-Space Projection Method for Stochastic Optimization Problems with Group Sparse Regularization. - Wenjie Li, Chi-Hua Wang, Guang Cheng, Qifan Song:
Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization. - Sihong He, Songyang Han, Sanbao Su, Shuo Han, Shaofeng Zou, Fei Miao:
Robust Multi-Agent Reinforcement Learning with State Uncertainty. - Mark Ibrahim, Quentin Garrido, Ari S. Morcos, Diane Bouchacourt:
The Robustness Limits of SoTA Vision Models to Natural Variation. - Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage, Himabindu Lakkaraju:
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making. - Ali Ramezani-Kebrya, Fanghui Liu, Thomas Pethick, Grigorios Chrysos, Volkan Cevher:
Federated Learning under Covariate Shifts with Generalization Guarantees. - Yuji Roh, Weili Nie, De-An Huang, Steven Euijong Whang, Arash Vahdat, Anima Anandkumar:
Dr-Fairness: Dynamic Data Ratio Adjustment for Fair Training on Real and Generated Data. - Johanna Vielhaben, Stefan Bluecher, Nils Strodthoff:
Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees. - Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer:
Contextualize Me - The Case for Context in Reinforcement Learning. - Jorge A. Mendez, Eric Eaton:
How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition. - Hong-Xing Yu, Michelle Guo, Alireza Fathi, Yen-Yu Chang, Eric Ryan Chan, Ruohan Gao, Thomas A. Funkhouser, Jiajun Wu:
Learning Object-Centric Neural Scattering Functions for Free-viewpoint Relighting and Scene Composition. - Max Wasserman, Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro:
Learning Graph Structure from Convolutional Mixtures. - Kaspar Valk, Meelis Kull:
Assuming Locally Equal Calibration Errors for Non-Parametric Multiclass Calibration. - Ido Ben-Shaul, Tomer Galanti, Shai Dekel:
Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions. - Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Yacine Jernite, Margaret Mitchell, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, Leandro von Werra, Harm de Vries:
The Stack: 3 TB of permissively licensed source code. - Daniel Alabi, Omri Ben-Eliezer, Anamay Chaturvedi:
Bounded Space Differentially Private Quantiles. - Sherwin Bahmani, Jeong Joon Park, Despoina Paschalidou, Hao Tang, Gordon Wetzstein, Leonidas J. Guibas, Luc Van Gool, Radu Timofte:
3D-Aware Video Generation. - Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Yixuan Li, Junzhou Huang:
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions. - Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Köthe:
Finding Competence Regions in Domain Generalization. - Yatong Chen, Jialu Wang, Yang Liu:
Learning to Incentivize Improvements from Strategic Agents. - Sayna Ebrahimi, Sercan Ö. Arik, Tomas Pfister:
Test-Time Adaptation for Visual Document Understanding. - Calarina Muslimani, Alex Lewandowski, Dale Schuurmans, Matthew E. Taylor, Jun Luo:
Reinforcement Teaching. - Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang:
Attentional-Biased Stochastic Gradient Descent. - Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M. Oberman:
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods. - Zhiqi Bu, Hua Wang, Zongyu Dai, Qi Long:
On the Convergence and Calibration of Deep Learning with Differential Privacy. - Niladri S. Chatterji, Saminul Haque, Tatsunori Hashimoto:
Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification. - Jack Hogan, Niall M. Adams:
On Averaging ROC Curves. - Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas:
LEAD: Min-Max Optimization from a Physical Perspective. - Dexuan Zhang, Thomas Westfechtel, Tatsuya Harada:
Unsupervised Domain Adaptation via Minimized Joint Error. - James B. Simon, Madeline Dickens, Dhruva Karkada, Michael Robert DeWeese:
The Eigenlearning Framework: A Conservation Law Perspective on Kernel Ridge Regression and Wide Neural Networks. - Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas:
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. - Nathan H. Ng, Neha Hulkund, Kyunghyun Cho, Marzyeh Ghassemi:
Predicting Out-of-Domain Generalization with Neighborhood Invariance. - Xueying Zhan, Zeyu Dai, Qingzhong Wang, Qing Li, Haoyi Xiong, Dejing Dou, Antoni B. Chan:
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios. - Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland:
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes. - Cameron Smith, Hong-Xing Yu, Sergey Zakharov, Frédo Durand, Joshua B. Tenenbaum, Jiajun Wu, Vincent Sitzmann:
Unsupervised Discovery and Composition of Object Light Fields. - Robert E. Tillman, Tucker Balch, Manuela Veloso:
Privacy-Preserving Energy-Based Generative Models for Marginal Distribution Protection. - Abdulkadir Canatar, Evan Peters, Cengiz Pehlevan, Stefan M. Wild, Ruslan Shaydulin:
Bandwidth Enables Generalization in Quantum Kernel Models. - Wonyeol Lee, Rahul Sharma, Alex Aiken:
Training with Mixed-Precision Floating-Point Assignments. - Carles Domingo-Enrich, Aram-Alexandre Pooladian:
An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow. - Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard:
TransFool: An Adversarial Attack against Neural Machine Translation Models. - Jiaxin Bai, Tianshi Zheng, Yangqiu Song:
Sequential Query Encoding for Complex Query Answering on Knowledge Graphs. - Iyiola E. Olatunji, Thorben Funke, Megha Khosla:
Releasing Graph Neural Networks with Differential Privacy Guarantees. - Yusuke Mukuta, Tatsuya Harada:
Invariant Feature Coding using Tensor Product Representation. - Florian Lalande, Kenji Doya:
Numerical Data Imputation for Multimodal Data Sets: A Probabilistic Nearest-Neighbor Kernel Density Approach. - Mauricio Delbracio, Peyman Milanfar:
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration. - Ties van Rozendaal, Johann Brehmer, Yunfan Zhang, Reza Pourreza, Auke J. Wiggers, Taco Cohen:
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set. - Justus Isaiah Hibshman, Tim Weninger:
Inherent Limits on Topology-Based Link Prediction. - Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt:
SC2 Benchmark: Supervised Compression for Split Computing. - Junyi Zhu, Matthew B. Blaschko:
Improving Differentially Private SGD via Randomly Sparsified Gradients. - Sebastian Szyller, Rui Zhang, Jian Liu, N. Asokan:
On the Robustness of Dataset Inference. - Yu Huang, Yuan Cheng, Yingbin Liang, Longbo Huang:
Online Min-max Problems with Non-convexity and Non-stationarity. - Kilian Pfeiffer, Martin Rapp, Ramin Khalili, Jörg Henkel:
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization. - Kaiyu Yang, Jia Deng:
Learning Symbolic Rules for Reasoning in Quasi-Natural Language. - Jannik Kossen, Catalina Cangea, Eszter Vértes, Andrew Jaegle, Viorica Patraucean, Ira Ktena, Nenad Tomasev, Danielle Belgrave:
Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task. - Sepehr Elahi, Baran Atalar, Sevda Ögüt, Cem Tekin:
Contextual Combinatorial Multi-output GP Bandits with Group Constraints. - Dan Friedman, Adji Bousso Dieng:
The Vendi Score: A Diversity Evaluation Metric for Machine Learning. - Kirill Bykov, Mayukh Deb, Dennis Grinwald, Klaus-Robert Müller, Marina M.-C. Höhne:
DORA: Exploring Outlier Representations in Deep Neural Networks. - Ziyun Li, Jona Otholt, Ben Dai, Di Hu, Christoph Meinel, Haojin Yang:
Supervised Knowledge May Hurt Novel Class Discovery Performance. - Kaili Ma, Garry Yang, Han Yang, Yongqiang Chen, James Cheng:
Calibrating and Improving Graph Contrastive Learning. - Anna Hedström, Philine Lou Bommer, Kristoffer Knutsen Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. - Jiaming Liang, Yongxin Chen:
A Proximal Algorithm for Sampling. - Zixuan Liu, Liu Liu, Bingzhe Wu, Lanqing Li, Xueqian Wang, Bo Yuan, Peilin Zhao:
Dynamics Adapted Imitation Learning. - Russell Tsuchida, Cheng Soon Ong:
Stochastic gradient updates yield deep equilibrium kernels. - Antoine Villié, Philippe Veber, Yohann de Castro, Laurent Jacob:
Neural Networks beyond explainability: Selective inference for sequence motifs. - Qi Qi, Jiameng Lyu, Kung-Sik Chan, Er-Wei Bai, Tianbao Yang:
Stochastic Constrained DRO with a Complexity Independent of Sample Size. - Junhyun Nam, Sangwoo Mo, Jaeho Lee, Jinwoo Shin:
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling. - Marissa A. Weis, Laura Pede, Timo Lüddecke, Alexander S. Ecker:
Self-Supervised Graph Representation Learning for Neuronal Morphologies. - Xu Cai, Chi Thanh Lam, Jonathan Scarlett:
On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature. - Yixin Wang, Dhanya Sridhar, David M. Blei:
Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness. - Jun Song, Niao He, Lijun Ding, Chaoyue Zhao:
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods. - Shiwen Zhao, Guillermo Sapiro:
Consistent Collaborative Filtering via Tensor Decomposition. - Andreas Kirsch:
Black-Box Batch Active Learning for Regression. - Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ramakanth Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, Thomas Scialom:
Augmented Language Models: a Survey. - Chunhui Zhang, Hongfu Liu, Jundong Li, Yanfang Ye, Chuxu Zhang:
Mind the Gap: Mitigating the Distribution Gap in Graph Few-shot Learning. - Tanguy Bosser, Souhaib Ben Taieb:
On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data. - Gregory Plumb, Nari Johnson, Ángel Alexander Cabrera, Ameet Talwalkar:
Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models. - Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh:
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. - Yinglun Xu, Qi Zeng, Gagandeep Singh:
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning. - Zirui Liu, Kaixiong Zhou, Zhimeng Jiang, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu:
DSpar: An Embarrassingly Simple Strategy for Efficient GNN training and inference via Degree-based Sparsification. - Jie Zhu, Jiyang Qi, Mingyu Ding, Xiaokang Chen, Ping Luo, Xinggang Wang, Wenyu Liu, Leye Wang, Jingdong Wang:
Understanding Self-Supervised Pretraining with Part-Aware Representation Learning. - Antoine Houdard, Arthur Leclaire, Nicolas Papadakis, Julien Rabin:
On the Gradient Formula for learning Generative Models with Regularized Optimal Transport Costs. - Oleg Arenz, Philipp Dahlinger, Zihan Ye, Michael Volpp, Gerhard Neumann:
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models. - Sofia Ek, Dave Zachariah, Fredrik D. Johansson, Peter Stoica:
Off-Policy Evaluation with Out-of-Sample Guarantees. - Romann M. Weber:
The Score-Difference Flow for Implicit Generative Modeling. - Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue, Satinder Singh, Tom Zahavy:
POMRL: No-Regret Learning-to-Plan with Increasing Horizons. - Adrian Perez-Suay, Paula Gordaliza, Jean-Michel Loubes, Dino Sejdinovic, Gustau Camps-Valls:
Fair Kernel Regression through Cross-Covariance Operators. - Sherwin Bahmani, Oliver Hahn, Eduard Zamfir, Nikita Araslanov, Daniel Cremers, Stefan Roth:
Semantic Self-adaptation: Enhancing Generalization with a Single Sample. - Eseoghene Ben-Iwhiwhu, Saptarshi Nath, Praveen K. Pilly, Soheil Kolouri, Andrea Soltoggio:
Lifelong Reinforcement Learning with Modulating Masks. - Olukorede Fakorede, Ashutosh Kumar Nirala, Modeste Atsague, Jin Tian:
Vulnerability-Aware Instance Reweighting For Adversarial Training. - Matthieu Zimmer, Xuening Feng, Claire Glanois, Zhaohui Jiang, Jianyi Zhang, Paul Weng, Dong Li, Jianye Hao, Wulong Liu:
Differentiable Logic Machines. - Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings:
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science. - Naoya Takeishi, Yoshinobu Kawahara:
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores. - Fabian Altekrüger, Paul Hagemann, Gabriele Steidl:
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems. - Xuchan Bao, Guodong Zhang:
Finding and Only Finding Differential Nash Equilibria by Both Pretending to be a Follower. - Guillermo Ortiz-Jiménez, Pau de Jorge, Amartya Sanyal, Adel Bibi, Puneet K. Dokania, Pascal Frossard, Grégory Rogez, Philip Torr:
Catastrophic overfitting can be induced with discriminative non-robust features. - Noveen Sachdeva, Julian J. McAuley:
Data Distillation: A Survey. - Jaemin Yoo, Tiancheng Zhao, Leman Akoglu:
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success. - Huangjie Zheng, Xu Chen, Jiangchao Yao, Hongxia Yang, Chunyuan Li, Ya Zhang, Hao Zhang, Ivor W. Tsang, Jingren Zhou, Mingyuan Zhou
Contrastive Attraction and Contrastive Repulsion for Representation Learning. - Yang Li, Kun Xiong, Yingping Zhang, Jiangcheng Zhu, Stephen Marcus McAleer, Wei Pan, Jun Wang, Zonghong Dai, Yaodong Yang:
JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games. - Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael A. Osborne:
Bayesian Quadrature for Neural Ensemble Search. - Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar:
Assisting Human Decisions in Document Matching. - Hyungeun Lee, Kijung Yoon:
Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks. - Kyo Takano:
Self-Supervision is All You Need for Solving Rubik's Cube. - Iris R. Stone, Yotam Sagiv, Il Memming Park, Jonathan W. Pillow:
Spectral learning of Bernoulli linear dynamical systems models for decision-making. - Mateo Espinosa Zarlenga, Zohreh Shams, Michael E. Nelson, Been Kim, Mateja Jamnik:
TabCBM: Concept-based Interpretable Neural Networks for Tabular Data. - Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, Chandan K. Reddy:
Execution-based Code Generation using Deep Reinforcement Learning. - Thien Hang Nguyen, Hongyang R. Zhang, Huy L. Nguyen:
Improved Group Robustness via Classifier Retraining on Independent Splits. - Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, MohammadHossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab Mirrokni:
Tackling Provably Hard Representative Selection via Graph Neural Networks. - Dominic Masters, Josef Dean, Kerstin Kläser, Zhiyi Li, Samuel Maddrell-Mander, Adam Sanders, Hatem Helal, Deniz Beker, Andrew W. Fitzgibbon, Shenyang Huang, Ladislav Rampásek, Dominique Beaini:
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction. - Jiaojiao Fan, Shu Liu, Shaojun Ma, Haomin Zhou, Yongxin Chen:
Neural Monge Map estimation and its applications. - Chung-Yiu Yau, Hoi-To Wai:
DoCoM: Compressed Decentralized Optimization with Near-Optimal Sample Complexity. - Antoine Godichon-Baggioni, Nicklas Werge, Olivier Wintenberger:
Learning from time-dependent streaming data with online stochastic algorithms. - Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki:
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation. - Kiran Krishnamachari, See-Kiong Ng, Chuan-Sheng Foo:
Mitigating Real-World Distribution Shifts in the Fourier Domain. - Yue Niu, Zalan Fabian, Sunwoo Lee, Mahdi Soltanolkotabi, Salman Avestimehr:
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-scale Neural Network Optimization. - Shuaiqi Wang, Jonathan Hayase, Giulia Fanti, Sewoong Oh:
Towards a Defense Against Federated Backdoor Attacks Under Continuous Training. - Hanlin Yu, Marcelo Hartmann, Bernardo Williams, Arto Klami:
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics. - Batoul Taki, Anand D. Sarwate, Waheed U. Bajwa:
Structured Low-Rank Tensors for Generalized Linear Models. - Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Kostas Daniilidis, Edgar Dobriban:
Learning Augmentation Distributions using Transformed Risk Minimization. - Saba Dadsetan, Mohsen Hejrati, Shandong Wu, Somaye Hashemifar:
Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning. - Changlong Wu, Mohsen Heidari, Ananth Grama, Wojciech Szpankowski:
Expected Worst Case Regret via Stochastic Sequential Covering. - Maksim Makarenko, Elnur Gasanov, Abdurakhmon Sadiev, Rustem Islamov, Peter Richtárik:
Adaptive Compression for Communication-Efficient Distributed Training. - Arsenii Moskvichev, Victor Vikram Odouard, Melanie Mitchell:
The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain. - Benjamin Feuer, Ameya Joshi, Minh Pham, Chinmay Hegde:
Distributionally Robust Classification on a Data Budget. - Liyi Zhang, David M. Blei, Christian A. Naesseth:
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport. - Nikhil Vyas, Yamini Bansal, Preetum Nakkiran:
Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning. - Abhimanyu Das, Weihao Kong, Andrew Leach, Shaan Mathur, Rajat Sen, Rose Yu:
Long-term Forecasting with TiDE: Time-series Dense Encoder. - Snir Vitrack Tamam, Raz Lapid, Moshe Sipper:
Foiling Explanations in Deep Neural Networks. - Shiyu Liu, Rohan Ghosh, Chong Min John Tan, Mehul Motani:
Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks. - Elvis Nava, Seijin Kobayashi, Yifei Yin, Robert K. Katzschmann, Benjamin F. Grewe:
Meta-Learning via Classifier(-free) Diffusion Guidance. - Maxime Gasse, Damien Grasset, Guillaume Gaudron, Pierre-Yves Oudeyer:
Using Confounded Data in Latent Model-Based Reinforcement Learning. - Thomas Michel, Hossein Hajiabolhassan, Ronald Ortner:
Regret Bounds for Satisficing in Multi-Armed Bandit Problems. - Chi-Hua Wang, Wenjie Li, Guang Lin:
Federated High-Dimensional Online Decision Making. - Qiming Wu, Xiaohan Chen, Yifan Jiang, Zhangyang Wang:
Chasing Better Deep Image Priors between Over- and Under-parameterization. - Weicheng Kuo, A. J. Piergiovanni, Dahun Kim, Xiyang Luo, Benjamin Caine, Wei Li, Abhijit S. Ogale, Luowei Zhou, Andrew M. Dai, Zhifeng Chen, Claire Cui, Anelia Angelova:
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks. - Maxim Bonnaerens, Joni Dambre:
Learned Thresholds Token Merging and Pruning for Vision Transformers. - Rémi Leluc, François Portier:
Asymptotic Analysis of Conditioned Stochastic Gradient Descent. - Chuyu Zhang, Ruijie Xu, Xuming He:
Novel Class Discovery for Long-tailed Recognition. - Bryan M. Li, Isabel M. Cornacchia, Nathalie Rochefort, Arno Onken:
V1T: large-scale mouse V1 response prediction using a Vision Transformer. - Gaurav Maheshwari, Michaël Perrot:
FairGrad: Fairness Aware Gradient Descent. - Billy Joe Franks, Markus Anders, Marius Kloft, Pascal Schweitzer:
A Systematic Approach to Universal Random Features in Graph Neural Networks. - Antonio Longa, Veronica Lachi, Gabriele Santin, Monica Bianchini, Bruno Lepri, Pietro Lio, Franco Scarselli, Andrea Passerini:
Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities. - Owen Melia, Eric M. Jonas, Rebecca Willett:
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds. - Chrysoula Kosma, Giannis Nikolentzos, George Panagopoulos, Jean-Marc Steyaert, Michalis Vazirgiannis:
Neural Ordinary Differential Equations for Modeling Epidemic Spreading. - Stefan Sylvius Wagner, Peter Arndt, Jan Robine, Stefan Harmeling:
Cyclophobic Reinforcement Learning. - Zhilin Zhao, Longbing Cao:
Dual Representation Learning for Out-of-distribution Detection. - Mastane Achab, Réda Alami, Yasser Abdelaziz Dahou Djilali, Kirill Fedyanin, Eric Moulines:
One-Step Distributional Reinforcement Learning. - Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson:
Diffusion Models for Constrained Domains. - Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein:
Understanding convolution on graphs via energies. - Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda:
Holistic Evaluation of Language Models. - Ondrej Bohdal, Yongxin Yang, Timothy M. Hospedales:
Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error. - Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, Tommi S. Jaakkola:
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks. - Shanchao Yang, Kaili Ma, Baoxiang Wang, Tianshu Yu, Hongyuan Zha:
Learning to Boost Resilience of Complex Networks via Neural Edge Rewiring. - Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao:
Nonconvex-nonconcave min-max optimization on Riemannian manifolds. - Ludvig Killingberg, Helge Langseth:
The Multiquadric Kernel for Moment-Matching Distributional Reinforcement Learning. - Jayesh K. Gupta, Johannes Brandstetter:
Towards Multi-spatiotemporal-scale Generalized PDE Modeling. - Washim Uddin Mondal, Vaneet Aggarwal:
Reinforcement Learning with Delayed, Composite, and Partially Anonymous Reward. - Runze Li, Dahun Kim, Bir Bhanu, Weicheng Kuo:
RECLIP: Resource-efficient CLIP by Training with Small Images. - Erik Englesson, Amir Mehrpanah, Hossein Azizpour:
Logistic-Normal Likelihoods for Heteroscedastic Label Noise. - Wenqing Zheng, Edward W. Huang, Nikhil Rao, Zhangyang Wang, Karthik Subbian:
You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction. - Simon Buchholz:
Some Remarks on Identifiability of Independent Component Analysis in Restricted Function Classes. - Clément Lalanne, Aurélien Garivier, Rémi Gribonval:
About the Cost of Central Privacy in Density Estimation. - Joshua Mitton, Roderick Murray-Smith:
Subgraph Permutation Equivariant Networks. - Tim Hartill, Neset Tan, Michael Witbrock, Patricia J. Riddle:
Teaching Smaller Language Models To Generalise To Unseen Compositional Questions. - Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir:
On Adaptivity in Quantum Testing. - Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli:
An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-MDP. - Matej Zecevic, Moritz Willig, Devendra Singh Dhami, Kristian Kersting:
Causal Parrots: Large Language Models May Talk Causality But Are Not Causal. - Yuxuan Li, James L. McClelland:
Representations and Computations in Transformers that Support Generalization on Structured Tasks. - Jean-Christophe Gagnon-Audet, Kartik Ahuja, Mohammad-Javad Darvishi Bayazi, Pooneh Mousavi, Guillaume Dumas, Irina Rish:
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series. - Neeraj Gangwar, Nickvash Kani:
Semantic Representations of Mathematical Expressions in a Continuous Vector Space. - Luzian Lebovitz, Lukas Cavigelli, Michele Magno, Lorenz K. Müller:
Efficient Inference With Model Cascades. - Viraj Prabhu, David Acuna, Rafid Mahmood, Marc T. Law, Yuan-Hong Liao, Judy Hoffman, Sanja Fidler, James Lucas:
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting. - Alexander Luke Ian Norcliffe, Marc Peter Deisenroth:
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature. - Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn:
A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize Range. - Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, Huishuai Zhang:
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent. - Uzma Hasan, Emam Hossain, Md. Osman Gani:
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data. - Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava:
Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging. - Maria Bånkestad, Jens Sjölund, Jalil Taghia, Thomas B. Schön:
Variational Elliptical Processes. - Haonan Wang, Jieyu Zhang, Qi Zhu, Wei Huang, Kenji Kawaguchi, Xiaokui Xiao:
Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph. - Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc:
Fast Kernel Methods for Generic Lipschitz Losses via p-Sparsified Sketches. - Taro Makino, Yixin Wang, Krzysztof J. Geras, Kyunghyun Cho:
Detecting incidental correlation in multimodal learning via latent variable modeling. - Etienne David, Jean Bellot, Sylvain Le Corff:
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series. - Alexandre Piché, Valentin Thomas, Joseph Marino, Rafael Pardinas, Gian Maria Marconi, Christopher Pal, Mohammad Emtiyaz Khan:
Bridging the Gap Between Target Networks and Functional Regularization. - Toni Karvonen, Jon Cockayne, Filip Tronarp, Simo Särkkä:
A probabilistic Taylor expansion with Gaussian processes. - Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Lingjiong Zhu:
Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD than Constant Stepsize. - Cuong C. Nguyen, Thanh-Toan Do, Gustavo Carneiro:
Task Weighting in Meta-learning with Trajectory Optimisation. - Isaac Liao, Rumen Dangovski, Jakob Nicolaus Foerster, Marin Soljacic:
Learning to Optimize Quasi-Newton Methods. - Marek Herde, Denis Huseljic, Bernhard Sick:
Multi-annotator Deep Learning: A Probabilistic Framework for Classification. - Omkar Ranadive, Nikhil Thakurdesai, Ari S. Morcos, Matthew L. Leavitt, Stéphane Deny:
On the special role of class-selective neurons in early training. - Tim Dockhorn, Tianshi Cao, Arash Vahdat, Karsten Kreis:
Differentially Private Diffusion Models. - Alexander Luke Ian Norcliffe, Lev Proleev, Diana Mincu, Fletcher Lee Hartsell, Katherine A. Heller, Subhrajit Roy:
Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression. - Mina Lee, Megha Srivastava, Amelia Hardy, John Thickstun, Esin Durmus, Ashwin Paranjape, Ines Gerard-Ursin, Xiang Lisa Li, Faisal Ladhak, Frieda Rong, Rose E. Wang, Minae Kwon, Joon Sung Park, Hancheng Cao, Tony Lee, Rishi Bommasani, Michael S. Bernstein, Percy Liang:
Evaluating Human-Language Model Interaction. - Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Bernhard Schölkopf, Michael Curtis Mozer, Christopher Pal, Yoshua Bengio:
Neural Causal Structure Discovery from Interventions. - Armando J. Cabrera Pacheco, Robert C. Williamson:
The Geometry of Mixability. - Dovile Juodelyte, Amelia Jiménez-Sánchez, Veronika Cheplygina:
Revisiting Hidden Representations in Transfer Learning for Medical Imaging. - Si-An Chen, Chun-Liang Li, Sercan Ö. Arik, Nathanael C. Yoder, Tomas Pfister:
TSMixer: An All-MLP Architecture for Time Series Forecast-ing. - Shiyu Liu, Rohan Ghosh, Mehul Motani:
AP: Selective Activation for De-sparsifying Pruned Networks. - Uddeshya Upadhyay, Sairam Bade, Arjun Puranik, Shahir Asfahan, Melwin Babu, Francisco Lopez-Jimenez, Samuel J. Asirvatham, Ashim Prasad, Ajit Rajasekharan, Samir Awasthi, Rakesh Barve:
HypUC: Hyperfine Uncertainty Calibration with Gradient- boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms. - Kristjan H. Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien:
k-Mixup Regularization for Deep Learning via Optimal Transport. - Lawrence K. Saul:
Weight-balancing fixes and flows for deep learning. - Cormac Herley:
Approximating Naive Bayes on Unlabelled Categorical Data. - Thong Bach, Anh Tong, Truong Son Hy, Vu Nguyen, Thanh Nguyen-Tang:
Global Contrastive Learning for Long-Tailed Classification. - Jan Tönshoff, Martin Ritzert, Hinrikus Wolf, Martin Grohe:
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing. - Konstantina Bairaktari, Paul Langton, Huy L. Nguyen, Niklas Smedemark-Margulies, Jonathan R. Ullman:
Fair and Useful Cohort Selection. - Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi:
High Fidelity Neural Audio Compression. - Kartik Gupta, Marios Fournarakis, Matthias Reisser, Christos Louizos, Markus Nagel:
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices. - Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan:
Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits. - Julien Walden Huang, Stephen J. Roberts, Jan-Peter Calliess:
On the Sample Complexity of Lipschitz Constant Estimation. - Wenzhe Li, Hao Luo, Zichuan Lin, Chongjie Zhang, Zongqing Lu, Deheng Ye:
A Survey on Transformers in Reinforcement Learning. - Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frédéric Branchaud-Charron, Yarin Gal:
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning. - Takayuki Osa, Akinobu Hayashi, Pranav Deo, Naoki Morihira, Takahide Yoshiike:
Offline Reinforcement Learning with Mixture of Deterministic Policies. - Sina Baharlouei, Sze-Chuan Suen, Meisam Razaviyayn:
RIFLE: Imputation and Robust Inference from Low Order Marginals. - Joonas Jälkö, Lukas Prediger, Antti Honkela, Samuel Kaski:
DPVIm: Differentially Private Variational Inference Improved. - Zhen Qin, Weixuan Sun, Kaiyue Lu, Hui Deng, Dongxu Li, Xiaodong Han, Yuchao Dai, Lingpeng Kong, Yiran Zhong:
Linearized Relative Positional Encoding. - Julia Grosse, Cheng Zhang, Philipp Hennig:
Optimistic Optimization of Gaussian Process Samples. - Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada:
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data. - Si Chen, Yi Zeng, Won Park, Jiachen T. Wang, Xun Chen, Lingjuan Lyu, Zhuoqing Mao, Ruoxi Jia:
Turning a Curse into a Blessing: Enabling In-Distribution-Data-Free Backdoor Removal via Stabilized Model Inversion. - Shuichi Miyazawa, Daichi Mochihashi:
Estimating Differential Equations from Temporal Point Processes. - Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier, Dushyant Rao, Abbas Abdolmaleki, Ben Moran, Tuomas Haarnoja, Jan Humplik, Roland Hafner, Michael Neunert, Claudio Fantacci, Tim Hertweck, Thomas Lampe, Fereshteh Sadeghi, Nicolas Heess, Martin A. Riedmiller:
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration. - Xiao Luo, Yusheng Zhao, Zhengyang Mao, Yifang Qin, Wei Ju, Ming Zhang, Yizhou Sun:
RIGNN: A Rationale Perspective for Semi-supervised Open-world Graph Classification. - Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts? - Juan Cuesta-Albertos, Subhajit Dutta:
On Perfect Clustering for Gaussian Processes. - Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Daniel Rueckert, Georgios Kaissis:
Label Noise-Robust Learning using a Confidence-Based Sieving Strategy. - Ke Chen, Chunmei Wang, Haizhao Yang:
Deep Operator Learning Lessens the Curse of Dimensionality for PDEs. - Solveig Klepper, Ulrike von Luxburg:
Relating graph auto-encoders to linear models. - Sara Babakniya, Souvik Kundu, Saurav Prakash, Yue Niu, Salman Avestimehr:
Revisiting Sparsity Hunting in Federated Learning: Why does Sparsity Consensus Matter? - Shivani Bathla, Vinita Vasudevan:
IBIA: An Incremental Build-Infer-Approximate Framework for Approximate Inference of Partition Function. - Simon Föll, Alina Dubatovka, Eugen Ernst, Siu Lun Chau, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet:
Gated Domain Units for Multi-source Domain Generalization. - Yijia Wang, Matthias Poloczek, Daniel R. Jiang:
Dynamic Subgoal-based Exploration via Bayesian Optimization. - Andreas Kirsch:
Does 'Deep Learning on a Data Diet' reproduce? Overall yes, but GraNd at Initialization does not. - Xinyue Wang, Konrad P. Kording:
Learning domain-specific causal discovery from time series. - Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez:
Learning-to-defer for sequential medical decision-making under uncertainty. - Abulikemu Abuduweili, Changliu Liu:
An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms. - Aniruddha Saha, Shuhua Yu, Mohammad Sadegh Norouzzadeh, Wan-Yi Lin, Chaithanya Kumar Mummadi:
Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches. - Ting-Jui Chang, Sapana Chaudhary, Dileep Kalathil, Shahin Shahrampour:
Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems. - Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa:
Multiscale Causal Structure Learning. - Dominik Fay, Sindri Magnússon, Jens Sjölund, Mikael Johansson:
Adaptive Hyperparameter Selection for Differentially Private Gradient Descent. - Jing Wang, Laurel Hopkins, Tyler A. Hallman, W. Douglas Robinson, Rebecca A. Hutchinson:
Cross-validation for Geospatial Data: Estimating Generalization Performance in Geostatistical Problems. - Xinyu Zhang, Jiahui Chen, Junkun Yuan, Qiang Chen, Jian Wang, Xiaodi Wang, Shumin Han, Xiaokang Chen, Jimin Pi, Kun Yao, Junyu Han, Errui Ding, Jingdong Wang:
CAE v2: Context Autoencoder with CLIP Latent Alignment. - Enrico Fini, Pietro Astolfi, Adriana Romero-Soriano, Jakob Verbeek, Michal Drozdzal:
Improved baselines for vision-language pre-training. - Xinyu Yang, Huaxiu Yao, Allan Zhou, Chelsea Finn:
Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations. - Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi:
Projected Randomized Smoothing for Certified Adversarial Robustness. - Yue Niu, Saurav Prakash, Souvik Kundu, Sunwoo Lee, Salman Avestimehr:
Overcoming Resource Constraints in Federated Learning: Large Models Can Be Trained with only Weak Clients. - Calvin McCarter:
The Kernel Density Integral Transformation. - Rolf A. N. Starre, Marco Loog, Elena Congeduti, Frans A. Oliehoek:
An Analysis of Model-Based Reinforcement Learning From Abstracted Observations. - Alex Bie, Gautam Kamath, Guojun Zhang:
Private GANs, Revisited. - Alexander Tyurin, Lukang Sun, Konstantin Burlachenko, Peter Richtárik:
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling. - Wei Ju, Yifang Qin, Siyu Yi, Zhengyang Mao, Kangjie Zheng, Luchen Liu, Xiao Luo, Ming Zhang:
Zero-shot Node Classification with Graph Contrastive Embedding Network. - Mahalakshmi Sabanayagam, Pascal Mattia Esser, Debarghya Ghoshdastidar:
Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel. - Shruthi Gowda, Bahram Zonooz, Elahe Arani:
Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning. - Fatemeh Haghighi, Soumitra Ghosh, Sarah Chu, Hai Ngu, Mohsen Hejrati, Han Hui Lin, Baris Bingol, Somaye Hashemifar:
Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease. - Isidoros Tziotis, Zebang Shen, Ramtin Pedarsani, Hamed Hassani, Aryan Mokhtari:
Straggler-Resilient Personalized Federated Learning. - Ludvig Hult, Dave Zachariah, Peter Stoica:
Diagnostic Tool for Out-of-Sample Model Evaluation. - David Brellmann, David Filliat, Goran Frehse:
Fourier Features in Reinforcement Learning with Neural Networks. - Rustem Islamov, Xun Qian, Slavomír Hanzely, Mher Safaryan, Peter Richtárik:
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation. - Jwo-Yuh Wu, Liang-Chi Huang, Wen-Hsuan Li, Chun-Hung Liu, Rung-Hung Gau:
Greedier is Better: Selecting Multiple Neighbors per Iteration for Sparse Subspace Clustering. - Arnav Gangal, Luis Kim, Sean P. Carney:
Physics informed neural networks for elliptic equations with oscillatory differential operators. - Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic, Megha Khosla:
Multi-label Node Classification On Graph-Structured Data. - Clinton J. Wang, Polina Golland:
Discretization Invariant Networks for Learning Maps between Neural Fields. - Alison Pouplin, David Eklund, Carl Henrik Ek, Søren Hauberg:
Identifying latent distances with Finslerian geometry. - Romain Menegaux, Emmanuel Jehanno, Margot Selosse, Julien Mairal:
Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers. - Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, Alicia Parrish, Aaron Mueller, Ameya Prabhu, Euan McLean, Aaron Kirtland, Alexis Ross, Alisa Liu, Andrew Gritsevskiy, Daniel Wurgaft, Derik Kauffman, Gabriel Recchia, Jiacheng Liu, Joe Cavanagh, Max Weiss, Sicong Huang, The Floating Droid, Tom Tseng, Tomasz Korbak, Xudong Shen, Yuhui Zhang, Zhengping Zhou, Najoung Kim, Samuel R. Bowman, Ethan Perez:
Inverse Scaling: When Bigger Isn't Better. - Adrien Bolland, Gilles Louppe, Damien Ernst:
Policy Gradient Algorithms Implicitly Optimize by Continuation. - Liangke Gui, Yingshan Chang, Qiuyuan Huang, Subhojit Som, Alexander G. Hauptmann, Jianfeng Gao, Yonatan Bisk:
Training Vision-Language Transformers from Captions. - Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Irina Rish, Eugene Belilovsky:
Gradient Masked Averaging for Federated Learning. - Botao Hao, Rahul Jain, Dengwang Tang, Zheng Wen:
Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale. - Filippos Christianos, Georgios Papoudakis, Stefano V. Albrecht:
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning. - Nihal Murali, Aahlad Manas Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich:
Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics. - Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers:
Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs. - Kyle Matoba, Nikolaos Dimitriadis, François Fleuret:
Benefits of Max Pooling in Neural Networks: Theoretical and Experimental Evidence. - Shraman Pramanick, Li Jing, Sayan Nag, Jiachen Zhu, Hardik Shah, Yann LeCun, Rama Chellappa:
VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment. - Dheeraj Baby, Jianyu Xu, Yu-Xiang Wang:
Non-Stationary Contextual Pricing with Safety Constraints. - Yiwei Lu, Guojun Zhang, Sun Sun, Hongyu Guo, Yaoliang Yu:
f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning. - Michael McCabe, Peter Harrington, Shashank Subramanian, Jed Brown:
Towards Stability of Autoregressive Neural Operators. - David Stutz, Abhijit Guha Roy, Tatiana Matejovicova, Patricia Strachan, Ali Taylan Cemgil, Arnaud Doucet:
Conformal prediction under ambiguous ground truth. - Jingshi Lei, Da Li, Chengming Xu, Liming Fang, Timothy M. Hospedales, Yanwei Fu:
Worst-case Feature Risk Minimization for Data-Efficient Learning. - Kasra Darvish, Edward Raff, Francis Ferraro, Cynthia Matuszek:
Multimodal Language Learning for Object Retrieval in Low Data Regimes in the Face of Missing Modalities. - T. Anderson Keller, Xavier Suau, Luca Zappella:
Homomorphic Self-Supervised Learning. - Matej Zecevic, Devendra Singh Dhami, Kristian Kersting:
Not All Causal Inference is the Same. - Eloi Tanguy:
Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses. - Marc Lanctot, John Schultz, Neil Burch, Max Olan Smith, Daniel Hennes, Thomas Anthony, Julien Pérolat:
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning. - Leyang Zhang, Zhi-Qin John Xu, Tao Luo, Yaoyu Zhang:
Limitation of Characterizing Implicit Regularization by Data-independent Functions. - Sai Aparna Aketi, Sangamesh Kodge, Kaushik Roy:
Neighborhood Gradient Mean: An Efficient Decentralized Learning Method for Non-IID Data. - Marcus Klasson, Hedvig Kjellström, Cheng Zhang:
Learn the Time to Learn: Replay Scheduling in Continual Learning. - Shirong Xu, Chendi Wang, Will Wei Sun, Guang Cheng:
Binary Classification under Local Label Differential Privacy Using Randomized Response Mechanisms. - Dihong Jiang, Sun Sun:
DP-LFlow: Differentially Private Latent Flow for Scalable Sensitive Image Generation. - Wenhu Chen, Xueguang Ma, Xinyi Wang, William W. Cohen:
Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks. - Melika Behjati, James Henderson:
Inducing Meaningful Units from Character Sequences with Dynamic Capacity Slot Attention. - Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes, Ronald Kemker, Christopher Kanan:
SIESTA: Efficient Online Continual Learning with Sleep. - Wes Gurnee, Neel Nanda, Matthew Pauly, Katherine Harvey, Dmitrii Troitskii, Dimitris Bertsimas:
Finding Neurons in a Haystack: Case Studies with Sparse Probing. - Kang Zhao, Yijun Tan, Kai Han, Ting Hu, Hanting Chen, Tao Yuan, Yunhe Wang, Jun Yao:
Complementary Sparsity: Accelerating Sparse CNNs with High Accuracy on General-Purpose Computing Platforms. - Oliver Struckmeier, Ville Kyrki:
ILPO-MP: Mode Priors Prevent Mode Collapse when Imitating Latent Policies from Observations. - Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, David J. Fleet:
Synthetic Data from Diffusion Models Improves ImageNet Classification. - Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan:
Improving Continual Learning by Accurate Gradient Reconstructions of the Past. - Ziqing Fan, Jiangchao Yao, Ruipeng Zhang, Lingjuan Lyu, Yanfeng Wang, Ya Zhang:
Federated Learning under Partially Disjoint Data via Manifold Reshaping. - Muchen Li, Jeffrey Yunfan Liu, Leonid Sigal, Renjie Liao:
GraphPNAS: Learning Probabilistic Graph Generators for Neural Architecture Search. - Yifan Zhang, Hanlin Zhang, Zachary Chase Lipton, Li Erran Li, Eric P. Xing:
Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation. - Shai Feldman, Liran Ringel, Stephen Bates, Yaniv Romano:
Achieving Risk Control in Online Learning Settings. - Wilka Carvalho, Andrew Kyle Lampinen, Kyriacos Nikiforou, Felix Hill, Murray Shanahan:
Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning. - Kamran Chitsaz, Gonçalo Mordido, Jean-Pierre David, François Leduc-Primeau:
Training DNNs Resilient to Adversarial and Random Bit-Flips by Learning Quantization Ranges. - Runlong Zhou, Zelin He, Yuandong Tian, Yi Wu, Simon Shaolei Du:
Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization. - Takahiro Kawashima, Hideitsu Hino:
Minorization-Maximization for Learning Determinantal Point Processes. - Timothy Zee, Alexander Ororbia, Ankur Arjun Mali, Ifeoma Nwogu:
A Robust Backpropagation-Free Framework for Images. - Rickard Brüel Gabrielsson, Mikhail Yurochkin, Justin Solomon:
Rewiring with Positional Encodings for Graph Neural Networks. - Vaidotas Simkus, Michael U. Gutmann:
Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling. - Mariel A. Werner, Lie He, Michael I. Jordan, Martin Jaggi, Sai Praneeth Karimireddy:
Provably Personalized and Robust Federated Learning. - Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas:
Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize. - Toon Vanderschueren, Jeroen Berrevoets, Wouter Verbeke:
NOFLITE: Learning to Predict Individual Treatment Effect Distributions. - Eura Shin, Predrag Klasnja, Susan A. Murphy, Finale Doshi-Velez:
Online model selection by learning how compositional kernels evolve. - Chenjie Mao:
Offline Reinforcement Learning with Additional Covering Distributions. - Fadhel Ayed, Soufiane Hayou:
Data pruning and neural scaling laws: fundamental limitations of score-based algorithms. - Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister:
Invariant Structure Learning for Better Generalization and Causal Explainability. - Tony Liu, Patrick N. Lawlor, Lyle H. Ungar, Konrad P. Kording, Rahul Ladhania:
Automated Detection of Causal Inference Opportunities: Regression Discontinuity Subgroup Discovery. - Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne:
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks. - Jiate Liu, Yiqin Zhu, Kaiwen Xiao, Qiang Fu, Xiao Han, Wei Yang, Deheng Ye:
RLTF: Reinforcement Learning from Unit Test Feedback. - Shivakanth Sujit, Pedro H. M. Braga, Jörg Bornschein, Samira Ebrahimi Kahou:
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies. - Jiachen T. Wang, Si Chen, Ruoxi Jia:
One-Round Active Learning through Data Utility Learning and Proxy Models. - Yubei Chen, Adrien Bardes, Zengyi Li, Yann LeCun:
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning. - Gabriele D'Acunto, Gianmarco De Francisci Morales, Paolo Bajardi, Francesco Bonchi:
Learning Multiscale Non-stationary Causal Structures. - Yuchen Lu, Zhen Liu, Aristide Baratin, Romain Laroche, Aaron C. Courville, Alessandro Sordoni:
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods. - Louis Rouillard, Alexandre Le Bris, Thomas Moreau, Demian Wassermann:
PAVI: Plate-Amortized Variational Inference. - Heikki Timonen, Miika Aittala, Jaakko Lehtinen:
Invertible Hierarchical Generative Model for Images. - Niklas Åkerblom, Morteza Haghir Chehreghani:
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles. - Hanna Krasowski, Jakob Thumm, Marlon Müller, Lukas Schäfer, Xiao Wang, Matthias Althoff:
Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking. - Sumedh Pendurkar, Taoan Huang, Brendan Juba, Jiapeng Zhang, Sven Koenig, Guni Sharon:
The (Un)Scalability of Informed Heuristic Function Estimation in NP-Hard Search Problems. - Tiantian Zhang, Kevin Z. Shen, Zichuan Lin, Bo Yuan, Xueqian Wang, Xiu Li, Deheng Ye:
Replay-enhanced Continual Reinforcement Learning. - Leander Girrbach, Anders Christensen, Ole Winther, Zeynep Akata, A. Sophia Koepke:
Addressing caveats of neural persistence with deep graph persistence. - Zhiqi Bu, Yuan Zhang:
Differentially Private Optimizers Can Learn Adversarially Robust Models. - Michael Y. Hu, Angelica Chen, Naomi Saphra, Kyunghyun Cho:
Latent State Models of Training Dynamics. - Ruo Yang, Ping Liu, Mustafa Bilgic:
The Analysis of the Expected Change in the Classification Probability of the Predicted Label. - Goirik Chakrabarty, Manogna Sreenivas, Soma Biswas:
SANTA: Source Anchoring Network and Target Alignment for Continual Test Time Adaptation. - Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, Tong Zhang:
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment. - Mara Graziani, Laura O'Mahony, An-Phi Nguyen, Henning Müller, Vincent Andrearczyk:
Uncovering Unique Concept Vectors through Latent Space Decomposition. - El Houcine Bergou, Konstantin Burlachenko, Aritra Dutta, Peter Richtárik:
Personalized Federated Learning with Communication Compression. - Meng Xia, Ricardo Henao:
Reliable Active Learning via Influence Functions. - Daniel Nickelsen, Bubacarr Bah:
Improved identification accuracy in equation learning via comprehensive R2-elimination and Bayesian model selection. - Tao Sun, Qingsong Wang, Yunwen Lei, Dongsheng Li, Bao Wang:
Pairwise Learning with Adaptive Online Gradient Descent. - Altay Unal, Abdullah Akgül, Melih Kandemir, Gozde Unal:
Meta Continual Learning on Graphs with Experience Replay. - Arundhati Banerjee, Soham R. Phade, Stefano Ermon, Stephan Zheng:
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning. - Jonathan Scott, Michelle Yeo, Christoph H. Lampert:
Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning. - Thanh Duc Hoang, Do Viet Tung, Duy-Hung Nguyen, Bao-Sinh Nguyen, Huy Hoang Nguyen, Hung Le:
Universal Graph Continual Learning. - Julián Tachella, Laurent Jacques:
Learning to reconstruct signals from binary measurements alone. - Naibo Wang, Wenjie Feng, Yuchen Deng, Moming Duan, Fusheng Liu, See-Kiong Ng:
Data-Free Diversity-Based Ensemble Selection for One-Shot Federated Learning. - Avery Ma, Yangchen Pan, Amir-massoud Farahmand:
Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods. - Enric Boix-Adserà, Etai Littwin:
Tight conditions for when the NTK approximation is valid. - Katherine A. Keith, Sergey Feldman, David Jurgens, Jonathan Bragg, Rohit Bhattacharya:
RCT Rejection Sampling for Causal Estimation Evaluation. - Zhihong Deng, Jing Jiang, Guodong Long, Chengqi Zhang:
Causal Reinforcement Learning: A Survey. - Anson Lei, Bernhard Schölkopf, Ingmar Posner:
Variational Causal Dynamics: Discovering Modular World Models from Interventions. - Sravan Kumar Lalam, Hari Krishna Kunderu, Shayan Ghosh, Harish Kumar A., Samir Awasthi, Ashim Prasad, Francisco Lopez-Jimenez, Zachi Attia, Samuel J. Asirvatham, Paul A. Friedman, Rakesh Barve, Melwin Babu:
ECG Representation Learning with Multi-Modal EHR Data. - Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor:
Two-Level Actor-Critic Using Multiple Teachers. - Carolina Zheng, Keyon Vafa, David M. Blei:
Revisiting Topic-Guided Language Models. - Arnav Mohanty Das, Gantavya Bhatt, Megh Manoj Bhalerao, Vianne R. Gao, Rui Yang, Jeff A. Bilmes:
Accelerating Batch Active Learning Using Continual Learning Techniques. - Shirsha Bose, Ankit Jha, Hitesh Kandala, Biplab Banerjee:
Beyond Boundaries: A Novel Data-Augmentation Discourse for Open Domain Generalization. - Ronghang Zhu, Xiang Yu, Sheng Li:
Semi-Supervised Single Domain Generalization with Label-Free Adversarial Data Augmentation. - Chao Wang, Alexandre H. Thiery:
GIT-Net: Generalized Integral Transform for Operator Learning. - Sergey Troshin, Vlad Niculae:
Wrapped β-Gaussians with compact support for exact probabilistic modeling on manifolds. - Bilal Riaz, Yuksel Karahan, Austin J. Brockmeier:
Partial Optimal Transport for Support Subset Selection. - Thomas F. Burns, Robert Tang:
Detecting danger in gridworlds using Gromov's Link Condition. - Miles Everett, Mingjun Zhong, Georgios Leontidis:
ProtoCaps: A Fast and Non-Iterative Capsule Network Routing Method. - Dennis J. N. J. Soemers, Vegard Mella, Éric Piette, Matthew Stephenson, Cameron Browne, Olivier Teytaud:
Towards a General Transfer Approach for Policy-Value Networks. - Danny Panknin, Stefan Chmiela, Klaus-Robert Müller, Shinichi Nakajima:
Local Function Complexity for Active Learning via Mixture of Gaussian Processes. - Ella Tamir, Martin Trapp, Arno Solin:
Transport with Support: Data-Conditional Diffusion Bridges. - Max F. Burg, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, Chris Russell:
Image retrieval outperforms diffusion models on data augmentation. - Hengkang Wang, Taihui Li, Zhong Zhuang, Tiancong Chen, Hengyue Liang, Ju Sun:
Early Stopping for Deep Image Prior. - Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin:
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior. - Anshuman Chhabra, Kartik Patwari, Chandana Kuntala, Sristi, Deepak Kumar Sharma, Prasant Mohapatra:
Towards Fair Video Summarization. - Pranay Sharma, Rohan Panda, Gauri Joshi:
Federated Minimax Optimization with Client Heterogeneity. - Jacopo Teneggi, Beepul Bharti, Yaniv Romano, Jeremias Sulam:
SHAP-XRT: The Shapley Value Meets Conditional Independence Testing. - Ahmed Zerouali, Douglas B. Tweed:
Error bounds and dynamics of bootstrapping in actor-critic reinforcement learning. - Otmane Sakhi, David Rohde, Nicolas Chopin:
Fast Slate Policy Optimization: Going Beyond Plackett-Luce. - Xiaoyu Lin, Laurent Girin, Xavier Alameda-Pineda:
Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation. - Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy V, Jason T. Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries:
StarCoder: may the source be with you! - Andrew Melnik, Robin Schiewer, Moritz Lange, Andrei Ioan Muresanu, Mozhgan Saeidi, Animesh Garg, Helge J. Ritter:
Benchmarks for Physical Reasoning AI. - Marlon Tobaben, Aliaksandra Shysheya, John Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella Béguelin, Richard E. Turner, Antti Honkela:
On the Efficacy of Differentially Private Few-shot Image Classification. - Andreea Deac, Theophane Weber, George Papamakarios:
Equivariant MuZero. - Nianhui Guo, Joseph Bethge, Hong Guo, Christoph Meinel, Haojin Yang:
Towards Optimization-Friendly Binary Neural Network. - Jay P. Gala, Pranjal A. Chitale, Raghavan AK, Varun Gumma, Sumanth Doddapaneni, Aswanth Kumar M., Janki Atul Nawale, Anupama Sujatha, Ratish Puduppully, Vivek Raghavan, Pratyush Kumar, Mitesh M. Khapra, Raj Dabre, Anoop Kunchukuttan:
IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages. - Mustafa Shukor, Corentin Dancette, Alexandre Ramé, Matthieu Cord:
UnIVAL: Unified Model for Image, Video, Audio and Language Tasks. - Tianle Li, Max Ku, Cong Wei, Wenhu Chen:
DreamEdit: Subject-driven Image Editing. - Erum Mushtaq, Chaoyang He, Jie Ding, Salman Avestimehr:
Distributed Architecture Search Over Heterogeneous Distributions. - Ao Liu, Yu-Xiang Wang, Lirong Xia:
Smoothed Differential Privacy. - Jonas Pfeiffer, Sebastian Ruder, Ivan Vulic, Edoardo M. Ponti:
Modular Deep Learning. - Daniel Alabi, Chris Wiggins:
Privacy Budget Tailoring in Private Data Analysis. - Erfan Miahi, Revan MacQueen, Alex Ayoub, Abbas Masoumzadeh, Martha White:
Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning. - Jovita Lukasik, Paul Gavrikov, Janis Keuper, Margret Keuper:
Improving Native CNN Robustness with Filter Frequency Regularization. - Georg Bökman, Axel Flinth, Fredrik Kahl:
In search of projectively equivariant networks. - Alexander Telepov, Artem Tsypin, Kuzma Khrabrov, Sergey Yakukhnov, Pavel Strashnov, Petr Zhilyaev, Egor Rumiantsev, Daniel Ezhov, Manvel Avetisian, Olga Popova, Artur Kadurin:
FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction. - Soumya Suvra Ghosal, Souradip Chakraborty, Jonas Geiping, Furong Huang, Dinesh Manocha, Amrit S. Bedi:
A Survey on the Possibilities & Impossibilities of AI-generated Text Detection. - Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Tong Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell:
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.
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