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36th UAI 2020 (virtual online)
- Ryan P. Adams, Vibhav Gogate
:
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020, virtual online, August 3-6, 2020. Proceedings of Machine Learning Research 124, AUAI Press 2020 - Julius von Kügelgen, Alexander Mey
Semi-supervised learning, causality, and the conditional cluster assumption. 1-10 - Yue Wang, Shaofeng Zou:
Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise. 11-20 - Kento Nozawa, Pascal Germain, Benjamin Guedj:
PAC-Bayesian Contrastive Unsupervised Representation Learning. 21-30 - Eren Sezener, Peter Dayan:
Static and Dynamic Values of Computation in MCTS. 31-40 - Krikamol Muandet, Wittawat Jitkrittum, Jonas M. Kübler:
Kernel Conditional Moment Test via Maximum Moment Restriction. 41-50 - Thomas Flynn, Kwangmin Yu, Abid Malik, Nicholas D'Imperio, Shinjae Yoo:
Bounding the expected run-time of nonconvex optimization with early stopping. 51-60 - Ayman Boustati, Sattar Vakili, James Hensman, S. T. John:
Amortized variance reduction for doubly stochastic objective. 61-70 - Baekjin Kim, Ambuj Tewari:
Randomized Exploration for Non-Stationary Stochastic Linear Bandits. 71-80 - Ehsan Amid, Manfred K. Warmuth:
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. 81-90 - Hongjoon Ahn, Taesup Moon:
Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty. 91-100 - Andrew Holbrook, Shiwei Lan, Jeffrey Streets, Babak Shahbaba:
Nonparametric Fisher Geometry with Application to Density Estimation. 101-110 - Bradly C. Stadie, Lunjun Zhang, Jimmy Ba:
Learning Intrinsic Rewards as a Bi-Level Optimization Problem. 111-120 - Seyed Mohammad Asghari, Yi Ouyang, Ashutosh Nayyar:
Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems. 121-130 - Xu He, Haipeng Chen, Bo An:
Learning Behaviors with Uncertain Human Feedback. 131-140 - Yangyi Lu, Amirhossein Meisami, Ambuj Tewari, William Yan:
Regret Analysis of Bandit Problems with Causal Background Knowledge. 141-150 - Marco Eigenmann, Sach Mukherjee, Marloes H. Maathuis:
Evaluation of Causal Structure Learning Algorithms via Risk Estimation. 151-160 - Hoda Bidkhori, John Dickerson, Duncan C. McElfresh, Ke Ren:
Kidney Exchange with Inhomogeneous Edge Existence Uncertainty. 161-170 - Behzad Tabibian, Vicenç Gómez
On the design of consequential ranking algorithms. 171-180 - Yifang Chen, Alex Cuellar, Haipeng Luo, Jignesh Modi, Heramb Nemlekar, Stefanos Nikolaidis:
Fair Contextual Multi-Armed Bandits: Theory and Experiments. 181-190 - Sho Takemori, Masahiro Sato, Takashi Sonoda, Janmajay Singh, Tomoko Ohkuma:
Submodular Bandit Problem Under Multiple Constraints. 191-200 - Jialian Li, Yichi Zhou, Tongzheng Ren, Jun Zhu:
Exploration Analysis in Finite-Horizon Turn-based Stochastic Games. 201-210 - Kaiwen Zhou, Yanghua Jin, Qinghua Ding, James Cheng:
Amortized Nesterov's Momentum: A Robust Momentum and Its Application to Deep Learning. 211-220 - Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf:
Testing Goodness of Fit of Conditional Density Models with Kernels. 221-230 - Or Dinari, Oren Freifeld:
Scalable and Flexible Clustering of Grouped Data via Parallel and Distributed Sampling in Versatile Hierarchical Dirichlet Processes. 231-240 - Max Chickering:
Statistically Efficient Greedy Equivalence Search. 241-249 - Kai Zhou, Yevgeniy Vorobeychik:
Robust Collective Classification against Structural Attacks. 250-259 - Eric Hans Lee, David Eriksson, David Bindel, Bolong Cheng, Mike Mccourt:
Efficient Rollout Strategies for Bayesian Optimization. 260-269 - Zhuangyan Fang, Yangbo He:
IDA with Background Knowledge. 270-279 - Yifei Shen, Ye Xue, Jun Zhang, Khaled B. Letaief, Vincent Lau:
Complete Dictionary Learning via ℓp-norm Maximization. 280-289 - Yue Liu, Zhuangyan Fang, Yangbo He, Zhi Geng:
Collapsible IDA: Collapsing Parental Sets for Locally Estimating Possible Causal Effects. 290-299 - Sorawit Saengkyongam, Ricardo Silva:
Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders. 300-309 - Søren Wengel Mogensen:
Causal screening in dynamical systems. 310-319 - Diego Agudelo-España, Sebastián Gómez-González, Stefan Bauer, Bernhard Schölkopf, Jan Peters:
Bayesian Online Prediction of Change Points. 320-329 - Guohua Cheng, Hongli Ji, Yan Tian:
Walking on Two Legs: Learning Image Segmentation with Noisy Labels. 330-339 - Andrew Estornell, Sanmay Das, Edith Elkind, Yevgeniy Vorobeychik:
Election Control by Manipulating Issue Significance. 340-349 - Saeed Vahidian, Baharan Mirzasoleiman, Alexander Cloninger:
Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples. 350-359 - Ayan Mukhopadhyay, Kai Wang, Andrew Perrault, Mykel J. Kochenderfer, Milind Tambe, Yevgeniy Vorobeychik:
Robust Spatial-Temporal Incident Prediction. 360-369 - Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Lagrangian Decomposition for Neural Network Verification. 370-379 - Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori:
Robust modal regression with direct gradient approximation of modal regression risk. 380-389 - Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou:
A Simple Online Algorithm for Competing with Dynamic Comparators. 390-399 - Yu-Neng Chuang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai:
Skewness Ranking Optimization for Personalized Recommendation. 400-409 - Raj Kumar Maity, Arya Mazumdar, Soumyabrata Pal:
High Dimensional Discrete Integration over the Hypergrid. 410-419 - Pawel M. Chilinski, Ricardo Silva:
Neural Likelihoods via Cumulative Distribution Functions. 420-429 - Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour:
Unknown mixing times in apprenticeship and reinforcement learning. 430-439 - Nils Rethmeier, Vageesh Kumar Saxena, Isabelle Augenstein:
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP. 440-449 - Laura Niss, Ambuj Tewari:
What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination. 450-459 - Sikun Yang, Heinz Koeppl:
The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks. 460-469 - Priyank Agrawal, Theja Tulabandhula:
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect. 470-479 - Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek:
Compositional uncertainty in deep Gaussian processes. 480-489 - Zhimeng Pan, Zheng Wang, Shandian Zhe:
Streaming Nonlinear Bayesian Tensor Decomposition. 490-499 - Xi Wang, Junming Yin:
Relaxed Multivariate Bernoulli Distribution and Its Applications to Deep Generative Models. 500-509 - Jie Shen:
One-Bit Compressed Sensing via One-Shot Hard Thresholding. 510-519 - JBrandon Duck-Mayr
GPIRT: A Gaussian Process Model for Item Response Theory. 520-529 - Emilija Perkovic:
Identifying causal effects in maximally oriented partially directed acyclic graphs. 530-539 - Siamak Zamani Dadaneh, Shahin Boluki, Mingzhang Yin, Mingyuan Zhou
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator. 540-549 - Tengyang Xie, Nan Jiang:
Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison. 550-559 - Mingliang Chen, Min Wu:
Towards Threshold Invariant Fair Classification. 560-569 - Lirong Xia:
Optimal Statistical Hypothesis Testing for Social Choice. 570-579 - Tanner Fiez, Nihar B. Shah, Lillian J. Ratliff:
A SUPER* Algorithm to Optimize Paper Bidding in Peer Review. 580-589 - Alex Markham, Moritz Grosse-Wentrup:
Measurement Dependence Inducing Latent Causal Models. 590-599 - Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Marcel van Gerven, François Laviolette:
The Indian Chefs Process. 600-608 - Lalit Jain, Anna C. Gilbert, Umang Varma:
Spectral Methods for Ranking with Scarce Data. 609-618 - Basil Saeed, Anastasiya Belyaeva, Yuhao Wang, Caroline Uhler:
Anchored Causal Inference in the Presence of Measurement Error. 619-628 - Ao Liu, Yun Lu, Lirong Xia, Vassilis Zikas:
How Private Are Commonly-Used Voting Rules? 629-638 - Lovedeep Gondara, Ke Wang:
Differentially Private Small Dataset Release Using Random Projections. 639-648 - Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood:
Semi-supervised Sequential Generative Models. 649-658 - Hiroaki Sasaki, Takashi Takenouchi, Ricardo Pio Monti, Aapo Hyvärinen:
Robust contrastive learning and nonlinear ICA in the presence of outliers. 659-668 - Mengxiao Zhang, Fernando Beltrán, Jiamou Liu:
Selling Data at an Auction under Privacy Constraints. 669-678 - Adrien Dulac, Éric Gaussier, Christine Largeron:
Mixed-Membership Stochastic Block Models for Weighted Networks. 679-688 - Arnab Kumar Mondal, Sankalan Pal Chowdhury, Aravind Jayendran, Himanshu Asnani, Parag Singla, Prathosh A. P.:
MaskAAE: Latent space optimization for Adversarial Auto-Encoders. 689-698 - Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell:
Slice Sampling for General Completely Random Measures. 699-708 - Jingge Zhu:
Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful. 709-718 - Meet P. Vadera, Brian Jalaian, Benjamin M. Marlin:
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks. 719-728 - Ondrej Kuzelka:
Complex Markov Logic Networks: Expressivity and Liftability. 729-738 - Zhongyi Hu, Robin Evans:
Faster algorithms for Markov equivalence. 739-748 - Philips George John, Deepak Vijaykeerthy, Diptikalyan Saha:
Verifying Individual Fairness in Machine Learning Models. 749-758 - Yijue Dai, Tianjian Zhang, Zhidi Lin, Feng Yin, Sergios Theodoridis, Shuguang Cui:
An Interpretable and Sample Efficient Deep Kernel for Gaussian Process. 759-768 - Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy:
Amortized Bayesian Optimization over Discrete Spaces. 769-778 - Marko Järvenpää, Aki Vehtari, Pekka Marttinen:
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. 779-788 - Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Deep Sigma Point Processes. 789-798 - Amit Deshpande, Praneeth Kacham, Rameshwar Pratap:
Robust k-means++. 799-808 - Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci:
On Counterfactual Explanations under Predictive Multiplicity. 809-818 - Zhiqiang Xu, Ping Li:
A Practical Riemannian Algorithm for Computing Dominant Generalized Eigenspace. 819-828 - Aditya Modi, Ambuj Tewari:
No-regret Exploration in Contextual Reinforcement Learning. 829-838 - Jussi Viinikka, Mikko Koivisto:
Layering-MCMC for Structure Learning in Bayesian Networks. 839-848 - Arnab Kumar Mondal, Arnab Bhattacharjee, Sudipto Mukherjee, Himanshu Asnani, Sreeram Kannan, Prathosh A. P.:
C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation. 849-858 - Federico Tomasi, Praveen Chandar, Gal Levy-Fix, Mounia Lalmas-Roelleke, Zhenwen Dai:
Stochastic Variational Inference for Dynamic Correlated Topic Models. 859-868 - Wei Xing, Brian D. Ziebart:
Adversarial Learning for 3D Matching. 869-878 - Vincent Derkinderen, Evert Heylen, Pedro Zuidberg Dos Martires, Samuel Kolb, Luc De Raedt:
Ordering Variables for Weighted Model Integration. 879-888 - Giulia Denevi, Massimiliano Pontil, Dimitrios Stamos:
Online Parameter-Free Learning of Multiple Low Variance Tasks. 889-898 - Yichong Xu, Aparna Joshi, Aarti Singh, Artur Dubrawski:
Zeroth Order Non-convex optimization with Dueling-Choice Bandits. 899-908 - Travis Dick, Wesley Pegden, Maria-Florina Balcan:
Semi-bandit Optimization in the Dispersed Setting. 909-918 - Victor Veitch, Dhanya Sridhar, David M. Blei:
Adapting Text Embeddings for Causal Inference. 919-928 - Zhijian Ou, Yunfu Song:
Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models. 929-938 - Hermanni Hälvä, Aapo Hyvärinen:
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series. 939-948 - Numair Sani, Jaron J. R. Lee, Ilya Shpitser:
Identification and Estimation of Causal Effects Defined by Shift Interventions. 949-958 - San Gultekin, John W. Paisley:
Risk Bounds for Low Cost Bipartite Ranking. 959-968 - Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Boehmer, Shimon Whiteson:
Multitask Soft Option Learning. 969-978 - Konstantin Mishchenko, Filip Hanzely, Peter Richtárik:
99% of Worker-Master Communication in Distributed Optimization Is Not Needed. 979-988 - Gherardo Varando, Niels Richard Hansen:
Graphical continuous Lyapunov models. 989-998 - Carlos Améndola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu:
Structure Learning for Cyclic Linear Causal Models. 999-1008 - Krista Longi, Chang Rajani, Tom Sillanpää, Joni Mäkinen, Timo Rauhala, Ari Salmi, Edward Hæggström, Arto Klami:
Sensor Placement for Spatial Gaussian Processes with Integral Observations. 1009-1018 - Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric:
Active Model Estimation in Markov Decision Processes. 1019-1028 - Ellen R. Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, Joel Burdick:
Dueling Posterior Sampling for Preference-Based Reinforcement Learning. 1029-1038 - Chandler Squires, Yuhao Wang, Caroline Uhler:
Permutation-Based Causal Structure Learning with Unknown Intervention Targets. 1039-1048 - Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. 1049-1058 - Aisha Mohamed, Shameem Puthiya Parambath, Zoi Kaoudi, Ashraf Aboulnaga:
Popularity Agnostic Evaluation of Knowledge Graph Embeddings. 1059-1068 - Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi
Learning LWF Chain Graphs: A Markov Blanket Discovery Approach. 1069-1078 - Amur Ghose, Abdullah Rashwan, Pascal Poupart:
Batch norm with entropic regularization turns deterministic autoencoders into generative models. 1079-1088 - Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. 1089-1098 - Aurélien Bibaut, Antoine Chambaz, Mark J. van der Laan:
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits. 1099-1108 - Ricardo Silva Carvalho, Ke Wang, Lovedeep Gondara, Chunyan Miao:
Differentially Private Top-k Selection via Stability on Unknown Domain. 1109-1118 - Sayak Ray Chowdhury, Rafael Oliveira, Fabio Ramos:
Active Learning of Conditional Mean Embeddings via Bayesian Optimisation. 1119-1128 - Marcelo Hartmann, Georgi Agiashvili, Paul C. Bürkner, Arto Klami:
Flexible Prior Elicitation via the Prior Predictive Distribution. 1129-1138 - Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum:
Model-Augmented Conditional Mutual Information Estimation for Feature Selection. 1139-1148 - Jan Kretínský, Fabian Michel, Lukas Michel, Guillermo A. Pérez:
Finite-Memory Near-Optimal Learning for Markov Decision Processes with Long-Run Average Reward. 1149-1158 - Joris M. Mooij, Tom Claassen:
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles. 1159-1168 - Jan-Christian Hütter, Philippe Rigollet:
Estimation Rates for Sparse Linear Cyclic Causal Models. 1169-1178 - Tárik S. Salem, Helge Langseth, Heri Ramampiaro:
Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles. 1179-1187 - Honghua Zhang, Steven Holtzen, Guy Van den Broeck:
On the Relationship Between Probabilistic Circuits and Determinantal Point Processes. 1188-1197 - Matthew Wicker, Luca Laurenti, Andrea Patane, Marta Kwiatkowska:
Probabilistic Safety for Bayesian Neural Networks. 1198-1207 - Hanwen Xing, Geoff Nicholls, Jeong Lee:
Distortion estimates for approximate Bayesian inference. 1208-1217 - Michael Wan, Tanmay Gangwani, Jian Peng:
Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch. 1218-1227 - Aaron Zweig, Joan Bruna:
Provably Efficient Third-Person Imitation from Offline Observation. 1228-1237 - David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar:
Automated Dependence Plots. 1238-1247 - Jun Ho Yoon, Seyoung Kim:
EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices. 1248-1257 - Eric A. Hansen, Thomas Bowman:
Improved Vector Pruning in Exact Algorithms for Solving POMDPs. 1258-1267 - Tal Friedman, Guy Van den Broeck:
Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings. 1268-1277 - Luke B. Hewitt, Tuan Anh Le, Joshua B. Tenenbaum:
Learning to learn generative programs with Memoised Wake-Sleep. 1278-1287 - Chris Cundy, Stefano Ermon:
Flexible Approximate Inference via Stratified Normalizing Flows. 1288-1297 - Xinming Liu, Joseph Y. Halpern:
Bounded Rationality in Las Vegas: Probabilistic Finite Automata Play Multi-Armed Bandits. 1298-1307 - Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation. 1308-1317 - Soumyasundar Pal, Saber Malekmohammadi, Florence Regol, Yingxue Zhang, Yishi Xu, Mark Coates:
Non Parametric Graph Learning for Bayesian Graph Neural Networks. 1318-1327 - Ahmed Touati, Amy Zhang, Joelle Pineau, Pascal Vincent:
Stable Policy Optimization via Off-Policy Divergence Regularization. 1328-1337 - Beau Coker, Melanie Fernandes Pradier, Finale Doshi-Velez:
PoRB-Nets: Poisson Process Radial Basis Function Networks. 1338-1347 - Noam Finkelstein, Ilya Shpitser:
Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals. 1348-1357 - Ajay Jain, Pieter Abbeel, Deepak Pathak:
Locally Masked Convolution for Autoregressive Models. 1358-1367 - Rishabh Singh, José C. Príncipe:
Time Series Analysis using a Kernel based Multi-Modal Uncertainty Decomposition Framework. 1368-1377 - Hongyu Ren, Yuke Zhu, Jure Leskovec, Animashree Anandkumar, Animesh Garg:
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation. 1378-1387 - Jakob Runge:
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. 1388-1397
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