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