UAI 2019
118 papers
A Bayesian Approach to Robust Reinforcement Learning
Esther Derman, Daniel Mankowitz, Timothy Mann, Shie Mannor A Weighted Mini-Bucket Bound for Solving Influence Diagram
Junkyu Lee, Radu Marinescu, Alexander Ihler, Rina Dechter Active Multi-Information Source Bayesian Quadrature
Alexandra Gessner, Javier Gonzalez, Maren Mahsereci Adaptive Hashing for Model Counting
Jonathan Kuck, Tri Dao, Shengjia Zhao, Burak Bartan, Ashish Sabharwal, Stefano Ermon Approximate Causal Abstractions
Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern Block Neural Autoregressive Flow
Nicola De Cao, Wilker Aziz, Ivan Titov 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 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 Co-Training for Policy Learning
Jialin Song, Ravi Lanka, Yisong Yue, Masahiro Ono Countdown Regression: Sharp and Calibrated Survival Predictions
Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, Andrew Y. Ng 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 Efficient Multitask Feature and Relationship Learning
Han Zhao, Otilia Stretcu, Alexander J. Smola, Geoffrey J. Gordon Efficient Neural Network Verification with Exactness Characterization
Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Chongli Qin, Soham De, Pushmeet Kohli Empirical Mechanism Design: Designing Mechanisms from Data
Enrique Areyan Viqueira, Cyrus Cousins, Yasser Mohammad, Amy Greenwald Exclusivity Graph Approach to Instrumental Inequalities
Davide Poderini, Rafael Chaves, Iris Agresti, Gonzalo Carvacho, Fabio Sciarrino Guaranteed Scalable Learning of Latent Tree Models
Furong Huang, Niranjan Uma Naresh, Ioakeim Perros, Robert Chen, Jimeng Sun, Anima Anandkumar Interpretable Almost Matching Exactly with Instrumental Variables
M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky Intervening on Network Ties
Eli Sherman, Ilya Shpitser Learnability for the Information Bottleneck
Tailin Wu, Ian Fischer, Isaac L. Chuang, Max Tegmark Learning with Non-Convex Truncated Losses by SGD
Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang Low Frequency Adversarial Perturbation
Chuan Guo, Jared S. Frank, Kilian Q. Weinberger Noise Contrastive Priors for Functional Uncertainty
Danijar Hafner, Dustin Tran, Timothy Lillicrap, Alex Irpan, James Davidson Object Conditioning for Causal Inference
David Jensen, Javier Burroni, Matthew Rattigan On Densification for Minwise Hashing
Tung Mai, Anup Rao, Matt Kapilevich, Ryan Rossi, Yasin Abbasi-Yadkori, Ritwik Sinha P3O: Policy-on Policy-Off Policy Optimization
Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola Perturbed-History Exploration in Stochastic Linear Bandits
Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier Probability Distillation: A Caveat and Alternatives
Chin-Wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron Courville Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Xiaoting Shao, Martin Trapp, Kristian Kersting, Zoubin Ghahramani Randomized Value Functions via Multiplicative Normalizing Flows
Ahmed Touati, Harsh Satija, Joshua Romoff, Joelle Pineau, Pascal Vincent Real-Time Robotic Search Using Structural Spatial Point Processes
Olov Andersson, Per Sidén, Johan Dahlin, Patrick Doherty, Mattias Villani Sinkhorn AutoEncoders
Giorgio Patrini, Rianne Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen Subspace Inference for Bayesian Deep Learning
Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA
Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf The Role of Memory in Stochastic Optimization
Antonio Orvieto, Jonas Kohler, Aurelien Lucchi The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva Variational Sparse Coding
Francesco Tonolini, Bjørn Sand Jensen, Roderick Murray-Smith Variational Training for Large-Scale Noisy-or Bayesian Networks
Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth Wasserstein Fair Classification
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa