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Journal of Machine Learning Research, Volume 24
Volume 24, 2023
- Benjamin Moseley, Joshua R. Wang:
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search. 1:1-1:36 - Håvard Kvamme, Ørnulf Borgan:
The Brier Score under Administrative Censoring: Problems and a Solution. 2:1-2:26 - Leo L. Duan, George Michailidis, Mingzhou Ding:
Bayesian Spiked Laplacian Graphs. 3:1-3:35 - Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner:
Efficient Structure-preserving Support Tensor Train Machine. 4:1-4:22 - Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey:
Cluster-Specific Predictions with Multi-Task Gaussian Processes. 5:1-5:49 - Haifeng Jin, François Chollet, Qingquan Song, Xia Hu:
AutoKeras: An AutoML Library for Deep Learning. 6:1-6:6 - Tianhong Sheng, Bharath K. Sriperumbudur:
On Distance and Kernel Measures of Conditional Dependence. 7:1-7:16 - Radu Ioan Bot, Michael Sedlmayer, Phan Tu Vuong:
A Relaxed Inertial Forward-Backward-Forward Algorithm for Solving Monotone Inclusions with Application to GANs. 8:1-8:37 - Hanbaek Lyu, Facundo Mémoli, David Sivakoff:
Sampling random graph homomorphisms and applications to network data analysis. 9:1-9:79 - Michael O'Neill, Stephen J. Wright:
A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees. 10:1-10:34 - Vojtech Franc, Daniel Prusa, Václav Vorácek:
Optimal Strategies for Reject Option Classifiers. 11:1-11:49 - Emanuele Dolera, Stefano Favaro, Stefano Peluchetti:
Learning-augmented count-min sketches via Bayesian nonparametrics. 12:1-12:60 - Hédi Hadiji, Gilles Stoltz:
Adaptation to the Range in K-Armed Bandits. 13:1-13:33 - Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu:
Python package for causal discovery based on LiNGAM. 14:1-14:8 - Jon Vadillo, Roberto Santana, José Antonio Lozano:
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions. 15:1-15:42 - Cynthia Rudin, Yaron Shaposhnik:
Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation. 16:1-16:44 - Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi:
Learning Mean-Field Games with Discounted and Average Costs. 17:1-17:59 - Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis:
An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization. 18:1-18:41 - Konstantinos Perrakis, Thomas Lartigue
, Frank Dondelinger, Sach Mukherjee:
Regularized Joint Mixture Models. 19:1-19:47 - Tengyuan Liang, Benjamin Recht:
Interpolating Classifiers Make Few Mistakes. 20:1-20:27 - Pouya M. Ghari, Yanning Shen:
Graph-Aided Online Multi-Kernel Learning. 21:1-21:44 - Kaiyi Ji, Yingbin Liang:
Lower Bounds and Accelerated Algorithms for Bilevel Optimization. 22:1-22:56 - Eli N. Weinstein, Jeffrey W. Miller:
Bayesian Data Selection. 23:1-23:72 - Shai Feldman, Stephen Bates, Yaniv Romano:
Calibrated Multiple-Output Quantile Regression with Representation Learning. 24:1-24:48 - Cédric M. Campos
Discrete Variational Calculus for Accelerated Optimization. 25:1-25:33 - Hao Wang, Rui Gao, Flávio P. Calmon:
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels. 26:1-26:43 - Raj Agrawal, Tamara Broderick:
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time. 27:1-27:60 - Xuran Meng, Jeff Yao:
Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping. 28:1-28:40 - Fábio Malcher Miranda
HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn. 29:1-29:17 - Cody Lewis
Attacks against Federated Learning Defense Systems and their Mitigation. 30:1-30:50 - Shiyu Duan, Spencer Chang, José C. Príncipe:
Labels, Information, and Computation: Efficient Learning Using Sufficient Labels. 31:1-31:35 - Dimitris Bertsimas, Driss Lahlou Kitane:
Sparse PCA: a Geometric Approach. 32:1-32:33 - Boyu Wang, Jorge A. Mendez, Changjian Shui, Fan Zhou
Gap Minimization for Knowledge Sharing and Transfer. 33:1-33:57 - Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond. 34:1-34:11 - Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? 35:1-35:52 - Chao Xu, Hong Tao, Jing Zhang, Dewen Hu, Chenping Hou:
Label Distribution Changing Learning with Sample Space Expanding. 36:1-36:48 - Michael Unser:
Ridges, Neural Networks, and the Radon Transform. 37:1-37:33 - Michael I. Jordan, Tianyi Lin, Manolis Zampetakis
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. 38:1-38:46 - Julián Tachella, Dongdong Chen, Mike E. Davies:
Sensing Theorems for Unsupervised Learning in Linear Inverse Problems. 39:1-39:45 - Shaun M. Fallat, David G. Kirkpatrick, Hans Ulrich Simon, Abolghasem Soltani, Sandra Zilles:
On Batch Teaching Without Collusion. 40:1-40:33 - Shaowu Pan
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. 41:1-41:60 - Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. 42:1-42:63 - Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson:
Benchmarking Graph Neural Networks. 43:1-43:48 - Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng:
Robust Load Balancing with Machine Learned Advice. 44:1-44:46 - Nicolás García Trillos, Matt Jacobs, Jakwang Kim:
The multimarginal optimal transport formulation of adversarial multiclass classification. 45:1-45:56 - Tobias Fritz, Andreas Klingler:
The d-Separation Criterion in Categorical Probability. 46:1-46:49 - Haizi Yu, Igor Mineyev, Lav R. Varshney:
A Group-Theoretic Approach to Computational Abstraction: Symmetry-Driven Hierarchical Clustering. 47:1-47:61 - Xiaoyu Wang, Yaxiang Yuan:
On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size. 48:1-48:49 - Mridul Agarwal, Vaneet Aggarwal:
Reinforcement Learning for Joint Optimization of Multiple Rewards. 49:1-49:41 - Linxi Liu, Dangna Li, Wing Hung Wong:
Convergence Rates of a Class of Multivariate Density Estimation Methods Based on Adaptive Partitioning. 50:1-50:64 - Lingjun Li, Jun Li:
Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks. 51:1-51:44 - Kunal Pattanayak, Vikram Krishnamurthy:
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems. 52:1-52:64 - Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback. 53:1-53:45 - Chen Lu, Subhabrata Sen:
Contextual Stochastic Block Model: Sharp Thresholds and Contiguity. 54:1-54:34 - Shaogao Lv, Xin He, Junhui Wang:
Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches. 55:1-55:38 - Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch:
On the geometry of Stein variational gradient descent. 56:1-56:39 - Antoine Baker, Florent Krzakala
Tree-AMP: Compositional Inference with Tree Approximate Message Passing. 57:1-57:89 - Yan Shuo Tan, Roman Vershynin:
Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval. 58:1-58:47 - Ephy R. Love, Benjamin Filippenko, Vasileios Maroulas, Gunnar E. Carlsson:
Topological Convolutional Layers for Deep Learning. 59:1-59:35 - Qinbo Bai, Vaneet Aggarwal, Ather Gattami:
Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints. 60:1-60:25 - Christian Horvat, Jean-Pascal Pfister:
Density estimation on low-dimensional manifolds: an inflation-deflation approach. 61:1-61:37 - Kamélia Daudel, Randal Douc, François Roueff:
Monotonic Alpha-divergence Minimisation for Variational Inference. 62:1-62:76 - Marcelo Arenas, Pablo Barceló, Leopoldo E. Bertossi, Mikaël Monet:
On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results. 63:1-63:58 - Lan V. Truong:
Fundamental limits and algorithms for sparse linear regression with sublinear sparsity. 64:1-64:49 - Nikhil Iyer, V. Thejas, Nipun Kwatra, Ramachandran Ramjee, Muthian Sivathanu:
Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule. 65:1-65:37 - Gianluca Finocchio, Johannes Schmidt-Hieber:
Posterior Contraction for Deep Gaussian Process Priors. 66:1-66:49 - Eliezer de Souza da Silva
Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching. 67:1-67:51 - Ruoyu Wang, Miaomiao Su, Qihua Wang:
Distributed Nonparametric Regression Imputation for Missing Response Problems with Large-scale Data. 68:1-68:52 - Hau-Tieng Wu, Nan Wu:
When Locally Linear Embedding Hits Boundary. 69:1-69:80 - Jonathan Hillman, Toby Dylan Hocking:
Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection. 70:1-70:24 - Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau:
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition. 71:1-71:57 - Moran Feldman, Christopher Harshaw, Amin Karbasi:
How Do You Want Your Greedy: Simultaneous or Repeated? 72:1-72:87 - Chunlin Li, Xiaotong Shen, Wei Pan:
Inference for a Large Directed Acyclic Graph with Unspecified Interventions. 73:1-73:48 - Jordan Awan, Vinayak Rao:
Privacy-Aware Rejection Sampling. 74:1-74:32 - Ximena Fernández, Eugenio Borghini, Gabriel B. Mindlin, Pablo Groisman:
Intrinsic Persistent Homology via Density-based Metric Learning. 75:1-75:42 - Di Bo, Hoon Hwangbo, Vinit Sharma, Corey Arndt, Stephanie TerMaath:
A Randomized Subspace-based Approach for Dimensionality Reduction and Important Variable Selection. 76:1-76:31 - Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin:
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models. 77:1-77:42 - Glen Berseth, Florian Golemo, Christopher Pal:
Towards Learning to Imitate from a Single Video Demonstration. 78:1-78:26 - Snigdha Panigrahi, Peter W. MacDonald, Daniel Kessler:
Approximate Post-Selective Inference for Regression with the Group LASSO. 79:1-79:49 - Marlos C. Machado
Temporal Abstraction in Reinforcement Learning with the Successor Representation. 80:1-80:69 - Gaetano Romano
Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics. 81:1-81:36 - Ning Ning, Edward L. Ionides:
Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality. 82:1-82:76 - William J. Wilkinson, Simo Särkkä, Arno Solin:
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees. 83:1-83:50 - Xi Wang, Zhipeng Tu, Yiguang Hong, Yingyi Wu, Guodong Shi:
Online Optimization over Riemannian Manifolds. 84:1-84:67 - Henry Lam, Haofeng Zhang:
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence. 85:1-85:58 - George Stepaniants:
Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces. 86:1-86:72 - Shuang Zhou, Debdeep Pati, Tianying Wang, Yun Yang, Raymond J. Carroll:
Gaussian Processes with Errors in Variables: Theory and Computation. 87:1-87:53 - Yuqi Gu
Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data. 88:1-88:49 - Nikola B. Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs. 89:1-89:97 - Bernadette J. Stolz:
Outlier-Robust Subsampling Techniques for Persistent Homology. 90:1-90:35 - Likai Chen, Georg Keilbar, Wei Biao Wu:
Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds. 91:1-91:25 - Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang:
Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption. 92:1-92:46 - Yucheng Lu, Christopher De Sa:
Decentralized Learning: Theoretical Optimality and Practical Improvements. 93:1-93:62 - Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar:
Faith-Shap: The Faithful Shapley Interaction Index. 94:1-94:42 - Yunxiao Chen, Chengcheng Li, Jing Ouyang, Gongjun Xu:
Statistical Inference for Noisy Incomplete Binary Matrix. 95:1-95:66 - Jianhao Ma, Salar Fattahi:
Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization. 96:1-96:84 - Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu:
Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation. 97:1-97:30 - Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang:
An Analysis of Robustness of Non-Lipschitz Networks. 98:1-98:43 - Artem Vysogorets, Julia Kempe:
Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity. 99:1-99:23 - Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu:
FedLab: A Flexible Federated Learning Framework. 100:1-100:7 - Didong Li, Wenpin Tang, Sudipto Banerjee:
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds. 101:1-101:26 - Haixu Ma, Donglin Zeng, Yufeng Liu:
Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments. 102:1-102:48 - Michael R. Metel:
Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint. 103:1-103:44 - Mu Niu, Zhenwen Dai, Pokman Cheung, Yizhu Wang:
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics. 104:1-104:42 - Bahare Fatemi, Perouz Taslakian, David Vázquez, David Poole:
Knowledge Hypergraph Embedding Meets Relational Algebra. 105:1-105:34 - Lukas Trottner, Cathrine Aeckerle-Willems, Claudia Strauch:
Concentration analysis of multivariate elliptic diffusions. 106:1-106:38 - Olivier Coudray, Christine Keribin, Pascal Massart, Patrick Pamphile:
Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption. 107:1-107:31 - Michail Spitieris, Ingelin Steinsland:
Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors. 108:1-108:39 - Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu:
Dimensionless machine learning: Imposing exact units equivariance. 109:1-109:32 - Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi:
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. 110:1-110:43 - Tucker McElroy, Anindya Roy, Gaurab Hore:
FLIP: A Utility Preserving Privacy Mechanism for Time Series. 111:1-111:29 - Martijn Gösgens
The Hyperspherical Geometry of Community Detection: Modularity as a Distance. 112:1-112:36 - Ohad Shamir:
The Implicit Bias of Benign Overfitting. 113:1-113:40 - Xin Zou, Weiwei Liu:
Generalization Bounds for Adversarial Contrastive Learning. 114:1-114:54 - Chengzhuo Ni, Yaqi Duan, Munther A. Dahleh, Mengdi Wang, Anru R. Zhang:
Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition. 115:1-115:53 - Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, Chaochao Chen:
SQLFlow: An Extensible Toolkit Integrating DB and AI. 116:1-116:9 - Niladri S. Chatterji, Philip M. Long:
Deep linear networks can benignly overfit when shallow ones do. 117:1-117:39 - Manoj Kumar, Anurag Sharma, Sandeep Kumar:
A Unified Framework for Optimization-Based Graph Coarsening. 118:1-118:50 - Stefan Stein, Chenlei Leng:
An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks. 119:1-119:69 - Toni Karvonen, Chris J. Oates:
Maximum likelihood estimation in Gaussian process regression is ill-posed. 120:1-120:47 - Changhoon Song, Geonho Hwang, Junho Lee, Myungjoo Kang:
Minimal Width for Universal Property of Deep RNN. 121:1-121:41 - Brian R. Bartoldson, Bhavya Kailkhura, Davis W. Blalock:
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities. 122:1-122:77 - Alexander Tsigler, Peter L. Bartlett:
Benign overfitting in ridge regression. 123:1-123:76 - Weijie J. Su, Yuancheng Zhu:
HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation. 124:1-124:53 - Shaoyan Guo, Huifu Xu, Liwei Zhang:
Statistical Robustness of Empirical Risks in Machine Learning. 125:1-125:38 - Siddarth Asokan, Chandra Sekhar Seelamantula:
Euler-Lagrange Analysis of Generative Adversarial Networks. 126:1-126:100 - Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller:
Graph Clustering with Graph Neural Networks. 127:1-127:21 - Joshua Daniel Loyal, Yuguo Chen:
An Eigenmodel for Dynamic Multilayer Networks. 128:1-128:69 - Xinchi Qiu, Titouan Parcollet, Javier Fernández-Marqués, Pedro P. B. de Gusmao, Yan Gao, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane:
A First Look into the Carbon Footprint of Federated Learning. 129:1-129:23 - Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic:
Combinatorial Optimization and Reasoning with Graph Neural Networks. 130:1-130:61 - Patricia Wollstadt, Sebastian Schmitt, Michael Wibral:
A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition. 131:1-131:44 - Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi
Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data. 132:1-132:57 - Benjamin Jakubowski, Sriram Somanchi, Edward McFowland III
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators. 133:1-133:57 - Qian Li, Binyan Jiang, Defeng Sun:
MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation. 134:1-134:44 - Sheng Gao, Zongming Ma:
Sparse GCA and Thresholded Gradient Descent. 135:1-135:61 - Wenhao Li, Ningyuan Chen, L. Jeff Hong:
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection. 136:1-136:84 - Hui Jin, Guido Montúfar:
Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks. 137:1-137:97 - Andrew Davison, Morgane Austern:
Asymptotics of Network Embeddings Learned via Subsampling. 138:1-138:120 - Ben M. Hambly, Renyuan Xu, Huining Yang:
Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games. 139:1-139:56 - Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu:
Jump Interval-Learning for Individualized Decision Making with Continuous Treatments. 140:1-140:92 - Jian Li, Yong Liu, Weiping Wang:
Optimal Convergence Rates for Distributed Nystroem Approximation. 141:1-141:39 - Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith:
On Tilted Losses in Machine Learning: Theory and Applications. 142:1-142:79 - Nicolás García Trillos, Pengfei He, Chenghui Li:
Large sample spectral analysis of graph-based multi-manifold clustering. 143:1-143:71 - Noirrit Kiran Chandra
Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering. 144:1-144:42 - Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. 145:1-145:46 - Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White:
Off-Policy Actor-Critic with Emphatic Weightings. 146:1-146:63 - Joshua Cutler, Dmitriy Drusvyatskiy, Zaïd Harchaoui:
Stochastic Optimization under Distributional Drift. 147:1-147:56 - Zhishuai Guo, Yan Yan
Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition. 148:1-148:63 - Titouan Vayer, Rémi Gribonval:
Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning. 149:1-149:51 - Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Yong Yu, Jun Wang, Weinan Zhang:
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning. 150:1-150:12 - Tomer Levy, Felix Abramovich:
Generalization error bounds for multiclass sparse linear classifiers. 151:1-151:35 - Yiqun T. Chen, Daniela M. Witten:
Selective inference for k-means clustering. 152:1-152:41 - Cheng Zeng, Jeffrey W. Miller, Leo L. Duan:
Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process. 153:1-153:32 - Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd:
Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees. 154:1-154:48 - Tavor Z. Baharav, Tze Leung Lai:
Adaptive Data Depth via Multi-Armed Bandits. 155:1-155:29 - Giora Simchoni, Saharon Rosset:
Integrating Random Effects in Deep Neural Networks. 156:1-156:57 - Huan Li, Zhouchen Lin:
Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O(epsilon^(-7/4)) Complexity. 157:1-157:37 - Hamid Reza Feyzmahdavian, Mikael Johansson:
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees. 158:1-158:75 - Arnab Ganguly, Riten Mitra, Jinpu Zhou:
Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations. 159:1-159:39 - Shoaib Bin Masud, Matthew Werenski, James M. Murphy, Shuchin Aeron:
Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling. 160:1-160:65 - Yanwei Jia, Xun Yu Zhou:
q-Learning in Continuous Time. 161:1-161:61 - Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani:
Flexible Model Aggregation for Quantile Regression. 162:1-162:45 - Gavin Zhang, Salar Fattahi, Richard Y. Zhang:
Preconditioned Gradient Descent for Overparameterized Nonconvex Burer-Monteiro Factorization with Global Optimality Certification. 163:1-163:55 - David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart:
A Framework and Benchmark for Deep Batch Active Learning for Regression. 164:1-164:81 - Ibrahim Merad, Stéphane Gaïffas:
Robust Methods for High-Dimensional Linear Learning. 165:1-165:44 - Masaru Ito, Zhaosong Lu, Chuan He:
A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition. 166:1-166:34 - Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo:
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start. 167:1-167:37 - Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis, George Michailidis:
Inference on the Change Point under a High Dimensional Covariance Shift. 168:1-168:68 - Xuechan Li, Anthony D. Sung, Jichun Xie:
DART: Distance Assisted Recursive Testing. 169:1-169:41 - Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanic:
Small Transformers Compute Universal Metric Embeddings. 170:1-170:48 - Raphaël Berthier:
Incremental Learning in Diagonal Linear Networks. 171:1-171:26 - Ahmet Alacaoglu, Axel Böhm, Yura Malitsky:
Beyond the Golden Ratio for Variational Inequality Algorithms. 172:1-172:33 - Johannes Resin:
From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions. 173:1-173:21 - Xiao Fang, Malay Ghosh:
Posterior Consistency for Bayesian Relevance Vector Machines. 174:1-174:17 - Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang:
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity. 175:1-175:53 - Patrick F. Burauel:
Evaluating Instrument Validity using the Principle of Independent Mechanisms. 176:1-176:56 - Sevvandi Kandanaarachchi, Kate Smith-Miles:
Comprehensive Algorithm Portfolio Evaluation using Item Response Theory. 177:1-177:52 - Wenhao Li, Bo Jin, Xiangfeng Wang, Junchi Yan, Hongyuan Zha:
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning. 178:1-178:75 - Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, LSST Dark Energy Science Collaboration:
Variational Inference for Deblending Crowded Starfields. 179:1-179:36 - Jose H. Blanchet, Yang Kang, José Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang:
Dropout Training is Distributionally Robust Optimal. 180:1-180:60 - Zhen Zhang, Mohammed Haroon Dupty, Fan Wu, Javen Qinfeng Shi, Wee Sun Lee:
Factor Graph Neural Networks. 181:1-181:54 - Justin Grimmer, Dean Knox, Brandon M. Stewart:
Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding. 182:1-182:70 - Fenglei Fan, Rongjie Lai, Ge Wang:
Quasi-Equivalence between Width and Depth of Neural Networks. 183:1-183:22 - Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey:
Metrizing Weak Convergence with Maximum Mean Discrepancies. 184:1-184:20 - Rodrigue Siry, Ryan Webster, Loïc Simon, Julien Rabin:
On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity. 185:1-185:34 - Jun Shu, Deyu Meng, Zongben Xu:
Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks. 186:1-186:74 - Gecia Bravo Hermsdorff, Lee M. Gunderson, Pierre-André G. Maugis, Carey E. Priebe:
Quantifying Network Similarity using Graph Cumulants. 187:1-187:27 - Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen:
The Proximal ID Algorithm. 188:1-188:46 - Lukas Gonon:
Random Feature Neural Networks Learn Black-Scholes Type PDEs Without Curse of Dimensionality. 189:1-189:51 - Solveig Klepper, Christian Elbracht
Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees. 190:1-190:56 - Leena Chennuru Vankadara, Michael Lohaus, Siavash Haghiri, Faiz Ul Wahab, Ulrike von Luxburg:
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation. 191:1-191:83 - Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao:
PAC-learning for Strategic Classification. 192:1-192:38 - Ryan S. Y. Chan, Murray Pollock, Adam M. Johansen, Gareth O. Roberts:
Divide-and-Conquer Fusion. 193:1-193:82 - Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton:
MMD Aggregated Two-Sample Test. 194:1-194:81 - Santtu Tikka, Jouni Helske, Juha Karvanen:
Clustering and Structural Robustness in Causal Diagrams. 195:1-195:32 - Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann:
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data. 196:1-196:72 - Tetiana Gorbach, Xavier de Luna, Juha Karvanen, Ingeborg Waernbaum:
Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency. 197:1-197:65 - Adrien Pavao, Isabelle Guyon, Anne-Catherine Letournel, Dinh-Tuan Tran, Xavier Baró, Hugo Jair Escalante, Sergio Escalera, Tyler Thomas, Zhen Xu:
CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges. 198:1-198:6 - Ali Devran Kara, Naci Saldi, Serdar Yüksel:
Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity. 199:1-199:34 - Junsouk Choi, Yang Ni
Model-based Causal Discovery for Zero-Inflated Count Data. 200:1-200:32 - Junxiong Jia, Yanni Wu, Peijun Li, Deyu Meng:
Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations. 201:1-201:60 - Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff:
Multiplayer Performative Prediction: Learning in Decision-Dependent Games. 202:1-202:56 - Lili Su, Jiaming Xu, Pengkun Yang:
A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points. 203:1-203:48 - Yi-Rui Yang, Wu-Jun Li:
Buffered Asynchronous SGD for Byzantine Learning. 204:1-204:62 - Hussein Hazimeh, Rahul Mazumder, Tim Nonet:
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization. 205:1-205:8 - Peng Zhao, Yu-Hu Yan, Yu-Xiang Wang, Zhi-Hua Zhou:
Non-stationary Online Learning with Memory and Non-stochastic Control. 206:1-206:70 - Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
Augmented Sparsifiers for Generalized Hypergraph Cuts. 207:1-207:50 - Santiago Mazuelas, Mauricio Romero, Peter Grunwald:
Minimax Risk Classifiers with 0-1 Loss. 208:1-208:48 - Baijiong Lin, Yu Zhang:
LibMTL: A Python Library for Deep Multi-Task Learning. 209:1-209:7 - Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio:
GFlowNet Foundations. 210:1-210:55 - Fan Chen, Zhenjie Ren, Songbo Wang:
Entropic Fictitious Play for Mean Field Optimization Problem. 211:1-211:36 - Wei Liu, Xin Liu, Xiaojun Chen:
An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity. 212:1-212:43 - Marcel Wienöbst, Max Bannach, Maciej Liskiewicz:
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications. 213:1-213:45 - Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, Emma Strubell:
An Empirical Investigation of the Role of Pre-training in Lifelong Learning. 214:1-214:50 - Haili Zhang, Zhaobo Liu, Guohua Zou:
Least Squares Model Averaging for Distributed Data. 215:1-215:59 - Malte Londschien, Peter Bühlmann, Solt Kovács:
Random Forests for Change Point Detection. 216:1-216:45 - Yu-Jui Huang, Yuchong Zhang:
GANs as Gradient Flows that Converge. 217:1-217:40 - Jian Shen, Hang Lai, Minghuan Liu, Han Zhao, Yong Yu, Weinan Zhang:
Adaptation Augmented Model-based Policy Optimization. 218:1-218:35 - Hua Liu, Jinhong You, Jiguo Cao:
Functional L-Optimality Subsampling for Functional Generalized Linear Models with Massive Data. 219:1-219:41 - Wei-Fang Sun, Cheng-Kuang Lee, Simon See, Chun-Yi Lee:
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning. 220:1-220:32 - Doudou Zhou, Tianxi Cai, Junwei Lu:
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices. 221:1-221:43 - Mo Zhou, Jianfeng Lu:
Single Timescale Actor-Critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees. 222:1-222:34 - Bingqing Hu, Bin Nan:
Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data. 223:1-223:26 - Ben Dai, Chunlin Li:
RankSEG: A Consistent Ranking-based Framework for Segmentation. 224:1-224:50 - T. Mitchell Roddenberry, Santiago Segarra:
Limits of Dense Simplicial Complexes. 225:1-225:42 - Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu, Tian Lan, Wenzhuo Yang, Rowan Cassius, Doyen Sahoo, Devansh Arpit, Sri Subramanian, Gerald Woo, Amrita Saha, Arun Kumar Jagota, Gokulakrishnan Gopalakrishnan, Manpreet Singh, K. C. Krithika, Sukumar Maddineni, Dae-ki Cho, Bo Zong, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Steven C. H. Hoi, Huan Wang:
Merlion: End-to-End Machine Learning for Time Series. 226:1-226:6 - Binyan Jiang, Jialiang Li, Qiwei Yao:
Autoregressive Networks. 227:1-227:69 - Yunhua Xiang, Tianyu Zhang, Xu Wang, Ali Shojaie, Noah Simon:
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure. 228:1-228:38 - Connor Lawless, Sanjeeb Dash, Oktay Günlük, Dennis Wei:
Interpretable and Fair Boolean Rule Sets via Column Generation. 229:1-229:50 - Zhengyu Zhou, Wei Liu:
Sample Complexity for Distributionally Robust Learning under chi-square divergence. 230:1-230:27 - Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin:
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance. 231:1-231:37 - Xiaoyu Wang, Martin Benning
Lifted Bregman Training of Neural Networks. 232:1-232:51 - Max Olan Smith, Thomas W. Anthony, Michael P. Wellman:
Strategic Knowledge Transfer. 233:1-233:96 - Paul Pu Liang, Yiwei Lyu, Xiang Fan, Arav Agarwal
MultiZoo and MultiBench: A Standardized Toolkit for Multimodal Deep Learning. 234:1-234:7 - Ziyue Wang, Zhiqiang Tan:
Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models. 235:1-235:112 - Samuel N. Cohen, Deqing Jiang, Justin A. Sirignano:
Neural Q-learning for solving PDEs. 236:1-236:49 - Madhumitha Shridharan, Garud Iyengar:
Scalable Computation of Causal Bounds. 237:1-237:35 - M. E. J. Newman:
Efficient Computation of Rankings from Pairwise Comparisons. 238:1-238:25 - Oh-Ran Kwon, Hui Zou:
Leaky Hockey Stick Loss: The First Negatively Divergent Margin-based Loss Function for Classification. 239:1-239:40 - Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel:
PaLM: Scaling Language Modeling with Pathways. 240:1-240:113 - Zhuang Yang:
Improved Powered Stochastic Optimization Algorithms for Large-Scale Machine Learning. 241:1-241:29 - Andrea Cini, Daniele Zambon, Cesare Alippi:
Sparse Graph Learning from Spatiotemporal Time Series. 242:1-242:36 - Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet:
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics. 243:1-243:83 - Ying Jin, Emmanuel J. Candès:
Selection by Prediction with Conformal p-values. 244:1-244:41 - Yibo Yan, Xiaozhou Wang, Riquan Zhang:
Confidence Intervals and Hypothesis Testing for High-dimensional Quantile Regression: Convolution Smoothing and Debiasing. 245:1-245:49 - Kimon Fountoulakis, Amit Levi, Shenghao Yang, Aseem Baranwal
Graph Attention Retrospective. 246:1-246:52 - Mengyu Li, Jun Yu, Tao Li, Cheng Meng:
Importance Sparsification for Sinkhorn Algorithm. 247:1-247:44 - Philippe Gagnon, Florian Maire, Giacomo Zanella:
Improving multiple-try Metropolis with local balancing. 248:1-248:59 - Tianze Wang, Guanyang Wang
Unbiased Multilevel Monte Carlo Methods for Intractable Distributions: MLMC Meets MCMC. 249:1-249:40 - Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli:
Convex Reinforcement Learning in Finite Trials. 250:1-250:42 - Gautier Izacard, Patrick S. H. Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, Edouard Grave:
Atlas: Few-shot Learning with Retrieval Augmented Language Models. 251:1-251:43 - Xintao Xia, Zhanrui Cai:
Adaptive False Discovery Rate Control with Privacy Guarantee. 252:1-252:35 - Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat:
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. 253:1-253:15 - Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Simon Bøgh, Nestor Arana-Arexolaleiba:
skrl: Modular and Flexible Library for Reinforcement Learning. 254:1-254:9 - Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, Danny Abraham, Tony Givargis, Alexandru Nicolau, Alexander V. Veidenbaum:
Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures. 255:1-255:10 - Khurram Javed, Haseeb Shah
Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks. 256:1-256:34 - Hilde J. P. Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio:
Fairlearn: Assessing and Improving Fairness of AI Systems. 257:1-257:8 - Shiliang Sun, Jingjing Fei, Jing Zhao, Liang Mao
Multi-view Collaborative Gaussian Process Dynamical Systems. 258:1-258:32 - Ray Bai
Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance. 259:1-259:49 - James A. Grant, David S. Leslie:
Learning to Rank under Multinomial Logit Choice. 260:1-260:49 - Shounak Chattopadhyay, Antik Chakraborty, David B. Dunson:
Nearest Neighbor Dirichlet Mixtures. 261:1-261:46 - Shuxiao Chen, Qinqing Zheng, Qi Long, Weijie J. Su:
Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training? 262:1-262:59 - Lanjue Chen, Alan T. K. Wan, Shuyi Zhang, Yong Zhou:
Distributed Algorithms for U-statistics-based Empirical Risk Minimization. 263:1-263:43 - Dat Hong, Tong Wang, Stephen Baek:
ProtoryNet - Interpretable Text Classification Via Prototype Trajectories. 264:1-264:39 - Jue Hou, Zijian Guo, Tianxi Cai:
Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction. 265:1-265:58 - Zejian Liu, Meng Li:
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators. 266:1-266:37 - Dimitris Bertsimas, Ryan Cory-Wright, Nicholas A. G. Johnson:
Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach. 267:1-267:51 - Raaz Dwivedi, Chandan Singh, Bin Yu, Martin J. Wainwright:
Revisiting minimum description length complexity in overparameterized models. 268:1-268:59 - Eglantine Karlé, Hemant Tyagi:
Dynamic Ranking with the BTL Model: A Nearest Neighbor based Rank Centrality Method. 269:1-269:57 - Xiao-Tong Yuan, Ping Li:
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation. 270:1-270:52 - Y. Samuel Wang
Causal Discovery with Unobserved Confounding and Non-Gaussian Data. 271:1-271:61 - Yao Ji, Gesualdo Scutari, Ying Sun, Harsha Honnappa:
Distributed Sparse Regression via Penalization. 272:1-272:62 - Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou:
Online Non-stochastic Control with Partial Feedback. 273:1-273:50 - Kexin Jin, Jonas Latz, Chenguang Liu, Carola-Bibiane Schönlieb:
A Continuous-time Stochastic Gradient Descent Method for Continuous Data. 274:1-274:48 - Ingo Steinwart, Bharath K. Sriperumbudur, Philipp Thomann:
Adaptive Clustering Using Kernel Density Estimators. 275:1-275:56 - Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan:
On Biased Compression for Distributed Learning. 276:1-276:50 - Oskar Allerbo, Johan Jonasson, Rebecka Jörnsten:
Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net. 277:1-277:53 - Christoph Käding, Jakob Runge:
Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation. 278:1-278:144 - Guilherme Ost, Daniel Y. Takahashi:
Sparse Markov Models for High-dimensional Inference. 279:1-279:54 - Kun Yuan, Sulaiman A. Alghunaim, Xinmeng Huang:
Removing Data Heterogeneity Influence Enhances Network Topology Dependence of Decentralized SGD. 280:1-280:53 - Mohammad Emtiyaz Khan, Håvard Rue:
The Bayesian Learning Rule. 281:1-281:46 - Tianxi Li, Elizaveta Levina, Ji Zhu:
Community models for networks observed through edge nominations. 282:1-282:36 - Oscar López:
Near-Optimal Weighted Matrix Completion. 283:1-283:40 - Juan C. Perdomo, Akshay Krishnamurthy, Peter L. Bartlett, Sham M. Kakade:
A Complete Characterization of Linear Estimators for Offline Policy Evaluation. 284:1-284:50 - S. Hamid Mousavi, Jakob Drefs, Florian Hirschberger, Jörg Lücke:
Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables. 285:1-285:59 - Leo L. Duan, Zeyu Yuwen, George Michailidis, Zhengwu Zhang:
Low Tree-Rank Bayesian Vector Autoregression Models. 286:1-286:35 - Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Universal Approximation Property of Invertible Neural Networks. 287:1-287:68 - Yasin Abbasi-Yadkori, András György, Nevena Lazic:
A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits. 288:1-288:37 - Hoil Lee, Fadhel Ayed, Paul Jung
Deep Neural Networks with Dependent Weights: Gaussian Process Mixture Limit, Heavy Tails, Sparsity and Compressibility. 289:1-289:78 - Naofumi Hama, Masayoshi Mase, Art B. Owen:
Deletion and Insertion Tests in Regression Models. 290:1-290:38 - Shiyuan He, Hanxuan Ye, Kejun He:
A Unified Analysis of Multi-task Functional Linear Regression Models with Manifold Constraint and Composite Quadratic Penalty. 291:1-291:69 - Weijie Zheng, Benjamin Doerr:
From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms. 292:1-292:40 - Molei Liu, Yi Zhang, Katherine P. Liao, Tianxi Cai:
Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models. 293:1-293:50 - Louis-Philippe Vignault, Audrey Durand, Pascal Germain:
Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm. 294:1-294:13 - Quan Zhang, Yanxun Xu, Mei-Cheng Wang, Mingyuan Zhou:
Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-Varying Covariates. 295:1-295:43 - Jing Ouyang
High-Dimensional Inference for Generalized Linear Models with Hidden Confounding. 296:1-296:61 - Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Causal Bandits for Linear Structural Equation Models. 297:1-297:59 - Arrasy Rahman, Ignacio Carlucho, Niklas Höpner, Stefano V. Albrecht:
A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning. 298:1-298:74 - Erhan Bayraktar, Ibrahim Ekren, Xin Zhang:
A PDE approach for regret bounds under partial monitoring. 299:1-299:24 - Ion Matei, Maksym Zhenirovskyy, Johan de Kleer, John Maxwell III:
Sensitivity-Free Gradient Descent Algorithms. 300:1-300:26 - Karl Kunisch, Donato Vásquez-Varas
Learning Optimal Feedback Operators and their Sparse Polynomial Approximations. 301:1-301:38 - Ethan X. Fang, Yajun Mei, Yuyang Shi, Qunzhi Xu, Tuo Zhao:
Pivotal Estimation of Linear Discriminant Analysis in High Dimensions. 302:1-302:45 - Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett:
Random Feature Amplification: Feature Learning and Generalization in Neural Networks. 303:1-303:49 - Hanjia Gao, Xiaofeng Shao:
Two Sample Testing in High Dimension via Maximum Mean Discrepancy. 304:1-304:33 - Yuhua Zhu, Zachary Izzo, Lexing Ying:
Continuous-in-time Limit for Bayesian Bandits. 305:1-305:35 - Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang:
Multi-Consensus Decentralized Accelerated Gradient Descent. 306:1-306:50 - Guillaume Sagnol, Luc Pronzato:
Fast Screening Rules for Optimal Design via Quadratic Lasso Reformulation. 307:1-307:32 - Jörg Bornschein, Alexandre Galashov, Ross Hemsley, Amal Rannen-Triki, Yutian Chen, Arslan Chaudhry, Xu Owen He, Arthur Douillard, Massimo Caccia, Qixuan Feng, Jiajun Shen, Sylvestre-Alvise Rebuffi, Kitty Stacpoole, Diego de Las Casas, Will Hawkins, Angeliki Lazaridou, Yee Whye Teh, Andrei A. Rusu, Razvan Pascanu, Marc'Aurelio Ranzato:
Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research. 308:1-308:77 - Yu Liu, Degui Li, Yingcun Xia:
Dimension Reduction and MARS. 309:1-309:30 - Lukas Graf, Tobias Harks, Kostas Kollias, Michael Markl:
Prediction Equilibrium for Dynamic Network Flows. 310:1-310:33 - Jakob Robnik, Giuseppe Bruno De Luca, Eva Silverstein, Uros Seljak:
Microcanonical Hamiltonian Monte Carlo. 311:1-311:34 - Sam Corbett-Davies, Johann D. Gaebler, Hamed Nilforoshan, Ravi Shroff, Sharad Goel:
The Measure and Mismeasure of Fairness. 312:1-312:117 - Zi Xu, Ziqi Wang, Jun-Lin Wang, Yu-Hong Dai:
Zeroth-Order Alternating Gradient Descent Ascent Algorithms for A Class of Nonconvex-Nonconcave Minimax Problems. 313:1-313:25 - Jackson Zhou, John T. Ormerod, Clara Grazian:
Fast Expectation Propagation for Heteroscedastic, Lasso-Penalized, and Quantile Regression. 314:1-314:39 - Siyi Hu, Yifan Zhong, Minquan Gao, Weixun Wang, Hao Dong, Xiaodan Liang, Zhihui Li, Xiaojun Chang, Yaodong Yang:
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library. 315:1-315:23 - Peter L. Bartlett, Philip M. Long, Olivier Bousquet:
The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima. 316:1-316:36 - Nelvin Tan, Ramji Venkataramanan:
Mixed Regression via Approximate Message Passing. 317:1-317:44 - Samuel Lanthaler
Operator learning with PCA-Net: upper and lower complexity bounds. 318:1-318:67 - Pratik Patil, Jin-Hong Du, Arun Kumar Kuchibhotla:
Bagging in overparameterized learning: Risk characterization and risk monotonization. 319:1-319:113 - Subhadeep Paul, Olgica Milenkovic, Yuguo Chen:
Higher-Order Spectral Clustering Under Superimposed Stochastic Block Models. 320:1-320:58 - Cheolmin Kim, Youngseok Kim, Diego Klabjan:
Scale Invariant Power Iteration. 321:1-321:47 - Simge Küçükyavuz, Ali Shojaie, Hasan Manzour, Linchuan Wei, Hao-Hsiang Wu:
Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks. 322:1-322:38 - Aaron Sonabend W., Nilanjana Laha, Ashwin N. Ananthakrishnan, Tianxi Cai, Rajarshi Mukherjee:
Semi-Supervised Off-Policy Reinforcement Learning and Value Estimation for Dynamic Treatment Regimes. 323:1-323:86 - Lisa Bonheme, Marek Grzes:
Be More Active! Understanding the Differences Between Mean and Sampled Representations of Variational Autoencoders. 324:1-324:30 - Samuel Hess, Gregory Ditzler:
ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI. 325:1-325:49 - Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. 326:1-326:58 - Binglin Li, Changwon Yoon, Jeongyoun Ahn:
Reproducing Kernels and New Approaches in Compositional Data Analysis. 327:1-327:34 - Gábor Lugosi, Ciara Pike-Burke, Pierre-André Savalle:
Bandit problems with fidelity rewards. 328:1-328:44 - Inass Sekkat, Gabriel Stoltz:
Mini-batching error and adaptive Langevin dynamics. 329:1-329:58 - Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang:
The Power of Contrast for Feature Learning: A Theoretical Analysis. 330:1-330:78 - Shizhou Xu, Thomas Strohmer:
Fair Data Representation for Machine Learning at the Pareto Frontier. 331:1-331:63 - Tobias Uelwer, Sebastian Konietzny, Alexander Oberstraß, Stefan Harmeling:
Learning Conditional Generative Models for Phase Retrieval. 332:1-332:28 - Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten M. Borgwardt:
Weisfeiler and Leman go Machine Learning: The Story so far. 333:1-333:59 - Stephan Eckstein, Armin Iske, Mathias Trabs:
Dimensionality Reduction and Wasserstein Stability for Kernel Regression. 334:1-334:35 - Donghwan Lee, Xinmeng Huang, Hamed Hassani, Edgar Dobriban:
T-Cal: An Optimal Test for the Calibration of Predictive Models. 335:1-335:72 - Majid Mazouchi, Subramanya Nageshrao, Hamidreza Modares:
Finite-time Koopman Identifier: A Unified Batch-online Learning Framework for Joint Learning of Koopman Structure and Parameters. 336:1-336:35 - Seonghyun Jeong, Veronika Rocková:
The Art of BART: Minimax Optimality over Nonhomogeneous Smoothness in High Dimension. 337:1-337:65 - Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha:
Community Recovery in the Geometric Block Model. 338:1-338:53 - Marco C. Campi, Simone Garatti:
Compression, Generalization and Learning. 339:1-339:74 - Adam B. Kashlak
Topological Hidden Markov Models. 340:1-340:49 - Jacques Wainer:
A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets. 341:1-341:34 - Robert C. Williamson, Zac Cranko:
The Geometry and Calculus of Losses. 342:1-342:72 - You Zhao, Xiaofeng Liao, Xing He, Mingliang Zhou, Chaojie Li:
Accelerated Primal-Dual Mirror Dynamics for Centralized and Distributed Constrained Convex Optimization Problems. 343:1-343:59 - Tim Laux, Jona Lelmi:
Large data limit of the MBO scheme for data clustering: convergence of the dynamics. 344:1-344:49 - John Harlim, Shixiao Willing Jiang, John Wilson Peoples:
Radial Basis Approximation of Tensor Fields on Manifolds: From Operator Estimation to Manifold Learning. 345:1-345:85 - Johannes Kirschner, Tor Lattimore, Andreas Krause:
Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications. 346:1-346:45 - Haifeng Wang, Jinchi Chen, Ke Wei:
Implicit Regularization and Entrywise Convergence of Riemannian Optimization for Low Tucker-Rank Tensor Completion. 347:1-347:84 - Matteo Sesia, Stefano Favaro, Edgar Dobriban:
Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queries. 348:1-348:80 - Songyan Hou, Parnian Kassraie, Anastasis Kratsios, Andreas Krause, Jonas Rothfuss:
Instance-Dependent Generalization Bounds via Optimal Transport. 349:1-349:51 - Xiaolong Cui, Lei Shi, Wei Zhong, Changliang Zou:
Robust High-Dimensional Low-Rank Matrix Estimation: Optimal Rate and Data-Adaptive Tuning. 350:1-350:57 - Ying Jin, Dominik Rothenhäusler:
Modular Regression: Improving Linear Models by Incorporating Auxiliary Data. 351:1-351:52 - Yang Chen, Ziyan Luo:
Group SLOPE Penalized Low-Rank Tensor Regression. 352:1-352:30 - Guergana Petrova, Przemyslaw Wojtaszczyk:
Limitations on approximation by deep and shallow neural networks. 353:1-353:38 - Ehsan Mokhtarian
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models. 354:1-354:31 - Thijs Vogels, Hadrien Hendrikx, Martin Jaggi:
Beyond Spectral Gap: The Role of the Topology in Decentralized Learning. 355:1-355:31 - Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaïd Harchaoui:
MAUVE Scores for Generative Models: Theory and Practice. 356:1-356:92 - Jonathan W. Siegel:
Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces. 357:1-357:52 - Xiaonan Hu, Xinyu Zhang:
Optimal Parameter-Transfer Learning by Semiparametric Model Averaging. 358:1-358:53 - Danny Wood, Tingting Mu, Andrew M. Webb, Henry W. J. Reeve, Mikel Luján, Gavin Brown:
A Unified Theory of Diversity in Ensemble Learning. 359:1-359:49 - Hidde Fokkema, Rianne de Heide, Tim van Erven:
Attribution-based Explanations that Provide Recourse Cannot be Robust. 360:1-360:37 - Daniel Alabi, Salil P. Vadhan:
Differentially Private Hypothesis Testing for Linear Regression. 361:1-361:50 - Nadir Durrani, Fahim Dalvi, Hassan Sajjad:
Discovering Salient Neurons in deep NLP models. 362:1-362:40 - Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco:
Avalanche: A PyTorch Library for Deep Continual Learning. 363:1-363:6 - Gabriel Laberge, Yann Pequignot, Alexandre Mathieu, Foutse Khomh, Mario Marchand:
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set. 364:1-364:50 - Paula Harder, Alex Hernández-García, Venkatesh Ramesh, Qidong Yang, Prasanna Sattegeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick:
Hard-Constrained Deep Learning for Climate Downscaling. 365:1-365:40 - Jeffrey Näf
Confidence and Uncertainty Assessment for Distributional Random Forests. 366:1-366:77 - Jie Ren, Xidong Feng, Bo Liu, Xuehai Pan, Yao Fu, Luo Mai, Yaodong Yang:
TorchOpt: An Efficient Library for Differentiable Optimization. 367:1-367:14 - Paul Maria Scheikl, Balázs Gyenes, Rayan Younis, Christoph Haas, Gerhard Neumann, Martin Wagner, Franziska Mathis-Ullrich:
LapGym - An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery. 368:1-368:42 - Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas:
A Permutation-Free Kernel Independence Test. 369:1-369:68 - Devanshu Agrawal, James Ostrowski:
Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations. 370:1-370:40 - Shaocong Ma, Ziyi Chen, Shaofeng Zou, Yi Zhou:
Decentralized Robust V-learning for Solving Markov Games with Model Uncertainty. 371:1-371:40 - Ben Chugg, Hongjian Wang, Aaditya Ramdas:
A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds. 372:1-372:61 - Cosmas Heiß, Ingo Gühring, Martin Eigel:
Multilevel CNNs for Parametric PDEs. 373:1-373:42 - Stefano Peluchetti:
Diffusion Bridge Mixture Transports, Schrödinger Bridge Problems and Generative Modeling. 374:1-374:51 - Zhou Wang, Xingye Qiao
Set-valued Classification with Out-of-distribution Detection for Many Classes. 375:1-375:39 - Xiong Zhou, Xianming Liu, Hanzhang Wang, Deming Zhai, Junjun Jiang, Xiangyang Ji:
On the Dynamics Under the Unhinged Loss and Beyond. 376:1-376:62 - Adam Roberts, Hyung Won Chung, Gaurav Mishra, Anselm Levskaya, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Kehang Han, Michelle Casbon, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, Andrea Gesmundo:
Scaling Up Models and Data with t5x and seqio. 377:1-377:8 - Akshayaa Magesh, Venugopal V. Veeravalli, Anirban Roy, Susmit Jha:
Principled Out-of-Distribution Detection via Multiple Testing. 378:1-378:35 - Bin Shi, Weijie Su, Michael I. Jordan:
On Learning Rates and Schrödinger Operators. 379:1-379:53 - Xiao Guo, Yixuan Qiu, Hai Zhang, Xiangyu Chang:
Randomized Spectral Co-Clustering for Large-Scale Directed Networks. 380:1-380:68 - Yuetian Luo, Anru R. Zhang:
Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence. 381:1-381:48 - Diego Delle Donne, Matthieu Kowalski, Leo Liberti:
A Novel Integer Linear Programming Approach for Global L0 Minimization. 382:1-382:28 - Xingdong Feng, Yuling Jiao, Lican Kang, Baqun Zhang, Fan Zhou:
Over-parameterized Deep Nonparametric Regression for Dependent Data with Its Applications to Reinforcement Learning. 383:1-383:40 - Quang Minh Nguyen, Hoang H. Nguyen
On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error. 384:1-384:41 - Zihao Li, Boyi Liu, Zhuoran Yang, Zhaoran Wang, Mengdi Wang:
Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning. 385:1-385:43 - Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause:
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice. 386:1-386:62 - Jia Gu, Song Xi Chen:
Distributed Statistical Inference under Heterogeneity. 387:1-387:57 - Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, Anima Anandkumar:
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries. 388:1-388:26 - Alice L'Huillier, Luke Travis, Ismaël Castillo, Kolyan Ray:
Semiparametric Inference Using Fractional Posteriors. 389:1-389:61 - Nahuel Statuto, Irene Unceta, Jordi Nin, Oriol Pujol:
A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers. 390:1-390:34 - Wentao Huang, Houbao Lu, Haizhang Zhang:
Hierarchical Kernels in Deep Kernel Learning. 391:1-391:30 - Eric Xia, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan:
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning. 392:1-392:43 - Haoyuan Sun, Khashayar Gatmiry, Kwangjun Ahn, Navid Azizan:
A Unified Approach to Controlling Implicit Regularization via Mirror Descent. 393:1-393:58 - Keshav Motwani, Daniela M. Witten:
Revisiting inference after prediction. 394:1-394:18 - Werner Zellinger
Adaptive Learning of Density Ratios in RKHS. 395:1-395:28 - Zekai Wang, Weiwei Liu:
RVCL: Evaluating the Robustness of Contrastive Learning via Verification. 396:1-396:43 - Leo L. Duan, David B. Dunson:
Bayesian Spanning Tree: Estimating the Backbone of the Dependence Graph. 397:1-397:44 - Miheer Dewaskar, John Palowitch, Mark He, Michael I. Love, Andrew B. Nobel:
Finding Groups of Cross-Correlated Features in Bi-View Data. 398:1-398:47 - Yue Deng, Zirui Wang, Xi Chen, Yin Zhang:
Boosting Multi-agent Reinforcement Learning via Contextual Prompting. 399:1-399:34 - Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, Percy Liang:
Foundation Models and Fair Use. 400:1-400:79
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