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35th UAI 2019: Tel Aviv, Israel
- Amir Globerson, Ricardo Silva:
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019. Proceedings of Machine Learning Research 115, AUAI Press 2019 - Naman Goel, Boi Faltings:
Personalized Peer Truth Serum for Eliciting Multi-Attribute Personal Data. 18-27 - Zheng Wang, Shandian Zhe:
Conditional Expectation Propagation. 28-37 - Martin Slawski, Mostafa Rahmani, Ping Li:
A Sparse Representation-Based Approach to Linear Regression with Partially Shuffled Labels. 38-48 - Xingguo Li, Haoming Jiang, Jarvis D. Haupt, Raman Arora, Han Liu, Mingyi Hong, Tuo Zhao:
On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About its Nonsmooth Loss Function. 49-59 - Tanvi Verma, Pradeep Varakantham:
Correlated Learning for Aggregation Systems. 60-70 - Patrick Forré, Joris M. Mooij:
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. 71-80 - Ronald Ortner, Pratik Gajane, Peter Auer:
Variational Regret Bounds for Reinforcement Learning. 81-90 - Olivier Gouvert, Thomas Oberlin, Cédric Févotte:
Recommendation from Raw Data with Adaptive Compound Poisson Factorization. 91-101 - Hao Xiong, Yuanzhen Guo, Yibo Yang, Nicholas Ruozzi
:
One-Shot Inference in Markov Random Fields. 102-112 - Yuhui Wang, Hao He, Xiaoyang Tan:
Truly Proximal Policy Optimization. 113-122 - Craig Innes, Alex Lascarides:
Learning Factored Markov Decision Processes with Unawareness. 123-133 - Tim Pearce, Russell Tsuchida, Mohamed Zaki, Alexandra Brintrup, Andy Neely:
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions. 134-144 - Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, Andrew Y. Ng:
Countdown Regression: Sharp and Calibrated Survival Predictions. 145-155 - Stefano Tracà, Weiyu Yan, Cynthia Rudin:
Reducing Exploration of Dying Arms in Mortal Bandits. 156-163 - Guojun Zhang, Pascal Poupart, George Trimponias:
Comparing EM with GD in Mixture Models of Two Components. 164-174 - Zhe Zeng, Guy Van den Broeck:
Efficient Search-Based Weighted Model Integration. 175-185 - Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen:
Causal Discovery with General Non-Linear Relationships using Non-Linear ICA. 186-195 - Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvári, Masrour Zoghi:
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback. 196-206 - Philipp Geiger, Michel Besserve, Justus Winkelmann, Claudius Proissl, Bernhard Schölkopf:
Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory. 207-216 - Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf:
The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA. 217-227 - Sinead A. Williamson, Mauricio Tec:
Random Clique Covers for Graphs with Local Density and Global Sparsity. 228-238 - Agniva Chowdhury, Jiasen Yang, Petros Drineas
Randomized Iterative Algorithms for Fisher Discriminant Analysis. 239-249 - Taoan Huang, Bohui Fang, Xiaohui Bei
Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests. 250-260 - Cong Xie, Oluwasanmi Koyejo, Indranil Gupta:
Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation. 261-270 - Jonathan Kuck, Tri Dao, Shenjia Zhao, Burak Bartan, Ashish Sabharwal, Stefano Ermon:
Adaptive Hashing for Model Counting. 271-280 - Weili Nie, Ankit Patel:
Towards a Better Understanding and Regularization of GAN Training Dynamics. 281-291 - Shoubo Hu, Kun Zhang, Zhitang Chen, Laiwan Chan:
Domain Generalization via Multidomain Discriminant Analysis. 292-302 - Juan Carlos Saborío, Joachim Hertzberg:
Efficient Planning Under Uncertainty with Incremental Refinement. 303-312 - Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Cubic Regularization with Momentum for Nonconvex Optimization. 313-322 - Karthik Abinav Sankararaman, Anand Louis, Navin Goyal:
Stability of Linear Structural Equation Models of Causal Inference. 323-333 - Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning. 334-344 - Sanghack Lee, Vasant G. Honavar:
Towards Robust Relational Causal Discovery. 345-355 - Antonio Orvieto, Jonas Kohler, Aurélien Lucchi:
The Role of Memory in Stochastic Optimization. 356-366 - Liam Li, Ameet Talwalkar:
Random Search and Reproducibility for Neural Architecture Search. 367-377 - Sinong Geng, Mladen Kolar, Oluwasanmi Koyejo:
Joint Nonparametric Precision Matrix Estimation with Confounding. 378-388 - Sanghack Lee, Juan D. Correa, Elias Bareinboim:
General Identifiability with Arbitrary Surrogate Experiments. 389-398 - Karen Ullrich, Rianne van den Berg, Marcus A. Brubaker, David J. Fleet, Max Welling:
Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem. 399-411 - Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas
Approximate Inference in Structured Instances with Noisy Categorical Observations. 412-421 - Ahmed Touati, Harsh Satija, Joshua Romoff, Joelle Pineau, Pascal Vincent:
Randomized Value Functions via Multiplicative Normalizing Flows. 422-432 - Yujia Xie, Xiangfeng Wang, Ruijia Wang, Hongyuan Zha:
A Fast Proximal Point Method for Computing Exact Wasserstein Distance. 433-453 - Qi She, Anqi Wu:
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks. 454-464 - Ke Sun, Piotr Koniusz, Zhen Wang:
Fisher-Bures Adversary Graph Convolutional Networks. 465-475 - Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Epsilon-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning. 476-485 - Anthony Tompkins, Fabio Ramos:
Periodic Kernel Approximation by Index Set Fourier Series Features. 486-496 - Krishnamurthy (Dj) Dvijotham, Robert Stanforth, Sven Gowal, Chongli Qin, Soham De, Pushmeet Kohli:
Efficient Neural Network Verification with Exactness Characterization. 497-507 - Robert Bamler, Farnood Salehi, Stephan Mandt:
Augmenting and Tuning Knowledge Graph Embeddings. 508-518 - Meet Taraviya, Shivaram Kalyanakrishnan:
A Tighter Analysis of Randomised Policy Iteration. 519-529 - Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier:
Perturbed-History Exploration in Stochastic Linear Bandits. 530-540 - Pan Xu, Felicia Gao, Quanquan Gu:
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. 541-551 - Xin Wang, Fisher Yu, Lisa Dunlap, Yi-An Ma, Ruth Wang, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez:
Deep Mixture of Experts via Shallow Embedding. 552-562 - Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. 563-573 - Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon:
Sliced Score Matching: A Scalable Approach to Density and Score Estimation. 574-584 - Tineke Blom, Stephan Bongers, Joris M. Mooij:
Beyond Structural Causal Models: Causal Constraints Models. 585-594 - Aadirupa Saha, Shreyas Sheshadri, Chiranjib Bhattacharyya:
Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits. 595-605 - Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern:
Approximate Causal Abstractions. 606-615 - Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva:
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. 616-626 - Christian Knoll, Franz Pernkopf:
Belief Propagation: Accurate Marginals or Accurate Partition Function - Where is the Difference? 627-636 - Benito van der Zander, Maciej Liskiewicz:
Finding Minimal d-separators in Linear Time and Applications. 637-647 - Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor:
A Bayesian Approach to Robust Reinforcement Learning. 648-658 - Guanghui Wang, Shiyin Lu, Lijun Zhang:
Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization. 659-668 - Adithya Raam Sankar, Prashant Doshi, Adam Goodie:
Evacuate or Not? A POMDP Model of the Decision Making of Individuals in Hurricane Evacuation Zones. 669-678 - Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist, Thomas B. Schön:
Probabilistic Programming for Birth-Death Models of Evolution Using an Alive Particle Filter with Delayed Sampling. 679-689 - Francesco Tonolini, Bjørn Sand Jensen, Roderick Murray-Smith:
Variational Sparse Coding. 690-700 - Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang:
Learning with Non-Convex Truncated Losses by SGD. 701-711 - Alexandra Gessner, Javier Gonzalez, Maren Mahsereci:
Active Multi-Information Source Bayesian Quadrature. 712-721 - Gaurush Hiranandani, Harvineet Singh, Prakhar Gupta, Iftikhar Ahamath Burhanuddin, Zheng Wen, Branislav Kveton:
Cascading Linear Submodular Bandits: Accounting for Position Bias and Diversity in Online Learning to Rank. 722-732 - Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav
Sinkhorn AutoEncoders. 733-743 - Samuel Kolb, Pedro Zuidberg Dos Martires, Luc De Raedt:
How to Exploit Structure while Solving Weighted Model Integration Problems. 744-754 - Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper:
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation. 755-765 - Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos:
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations. 766-776 - Han Zhao, Otilia Stretcu, Alexander J. Smola, Geoffrey J. Gordon:
Efficient Multitask Feature and Relationship Learning. 777-787 - Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier, Andrew Gordon Wilson:
Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning. 788-798 - Christopher Aicher, Nicholas J. Foti, Emily B. Fox:
Adaptively Truncating Backpropagation Through Time to Control Gradient Bias. 799-808 - Seong Jae Hwang, Ronak Mehta, Hyunwoo J. Kim, Sterling C. Johnson, Vikas Singh:
Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging. 809-819 - Lin F. Yang
Online Factorization and Partition of Complex Networks by Random Walk. 820-830 - Tung Mai, Anup Rao, Matt Kapilevich, Ryan A. Rossi, Yasin Abbasi-Yadkori, Ritwik Sinha:
On Densification for Minwise Hashing. 831-840 - Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee:
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. 841-851 - Bingshan Hu, Nishant A. Mehta, Jianping Pan:
Problem-dependent Regret Bounds for Online Learning with Feedback Graphs. 852-861 - Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa:
Wasserstein Fair Classification. 862-872 - Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth:
Variational Training for Large-Scale Noisy-OR Bayesian Networks. 873-882 - Furong Huang, U. N. Niranjan, Ioakeim Perros, Robert Chen, Jimeng Sun, Anima Anandkumar:
Guaranteed Scalable Learning of Latent Tree Models. 883-893 - Roman Pogodin, Tor Lattimore:
On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits. 894-904 - Danijar Hafner, Dustin Tran, Timothy P. Lillicrap, Alex Irpan, James Davidson:
Noise Contrastive Priors for Functional Uncertainty. 905-914 - Jonathan Bragg, Emma Brunskill:
Fake It Till You Make It: Learning-Compatible Performance Support. 915-924 - Smitha Milli, Anca D. Dragan:
Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning. 925-934 - Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Samuel Burden:
Convergence Analysis of Gradient-Based Learning in Continuous Games. 935-944 - Gregory W. Gundersen, Bianca Dumitrascu, Jordan T. Ash, Barbara E. Engelhardt:
End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations. 945-955 - Hiteshi Sharma, Mehdi Jafarnia-Jahromi, Rahul Jain:
Approximate Relative Value Learning for Average-reward Continuous State MDPs. 956-964 - Topi Talvitie, Aleksis Vuoksenmaa, Mikko Koivisto:
Exact Sampling of Directed Acyclic Graphs from Modular Distributions. 965-974 - Eli Sherman, Ilya Shpitser:
Intervening on Network Ties. 975-984 - Steven Holtzen, Todd D. Millstein, Guy Van den Broeck:
Generating and Sampling Orbits for Lifted Probabilistic Inference. 985-994 - Olov Andersson, Per Sidén, Johan Dahlin, Patrick Doherty, Mattias Villani:
Real-Time Robotic Search using Structural Spatial Point Processes. 995-1005 - Mahak Goindani, Jennifer Neville:
Social Reinforcement Learning to Combat Fake News Spread. 1006-1016 - Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola:
P3O: Policy-on Policy-off Policy Optimization. 1017-1027 - Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser:
Causal Inference Under Interference And Network Uncertainty. 1028-1038 - Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood:
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow. 1039-1049 - Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark:
Learnability for the Information Bottleneck. 1050-1060 - Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng:
Learning Belief Representations for Imitation Learning in POMDPs. 1061-1071 - David D. Jensen, Javier Burroni, Matthew J. Rattigan:
Object Conditioning for Causal Inference. 1072-1082 - Sudipto Mukherjee, Himanshu Asnani, Sreeram Kannan:
CCMI : Classifier based Conditional Mutual Information Estimation. 1083-1093 - Enrique Areyan Viqueira, Cyrus Cousins, Yasser Mohammad, Amy Greenwald:
Empirical Mechanism Design: Designing Mechanisms from Data. 1094-1104 - Ricardo Salmon, Pascal Poupart:
On the Relationship Between Satisfiability and Markov Decision Processes. 1105-1115 - M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Interpretable Almost Matching Exactly With Instrumental Variables. 1116-1126 - Chuan Guo, Jared S. Frank, Kilian Q. Weinberger:
Low Frequency Adversarial Perturbation. 1127-1137 - Ondrej Kuzelka, Jesse Davis:
Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption. 1138-1148 - Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins:
Identification In Missing Data Models Represented By Directed Acyclic Graphs. 1149-1158 - Junkyu Lee, Radu Marinescu, Alexander Ihler, Rina Dechter:
A Weighted Mini-Bucket Bound for Solving Influence Diagram. 1159-1168 - Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Subspace Inference for Bayesian Deep Learning. 1169-1179 - Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Off-Policy Policy Gradient with Stationary Distribution Correction. 1180-1190 - Jialin Song, Ravi Lanka, Yisong Yue, Masahiro Ono:
Co-training for Policy Learning. 1191-1201 - Koh Takeuchi, Yuichi Yoshida, Yoshinobu Kawahara:
Variational Inference of Penalized Regression with Submodular Functions. 1202-1211 - Chin-Wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron C. Courville:
Probability Distillation: A Caveat and Alternatives. 1212-1221 - Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low:
Bayesian Optimization with Binary Auxiliary Information. 1222-1232 - Duligur Ibeling, Thomas Icard:
On Open-Universe Causal Reasoning. 1233-1243 - Diego P. P. Mesquita, Paul Blomstedt, Samuel Kaski:
Embarrassingly Parallel MCMC using Deep Invertible Transformations. 1244-1252 - Yingzhen Yang, Jiahui Yu:
Fast Proximal Gradient Descent for A Class of Non-convex and Non-smooth Sparse Learning Problems. 1253-1262 - Nicola De Cao, Wilker Aziz, Ivan Titov:
Block Neural Autoregressive Flow. 1263-1273 - Davide Poderini, Rafael Chaves, Iris Agresti, Gonzalo Carvacho, Fabio Sciarrino:
Exclusivity Graph Approach to Instrumental Inequalities. 1274-1283
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