人工智能顶会UAI 2019大会论文接受列表
【导读】UAI大会全称为Conference on Uncertainty in Artificial Intelligence，立足于不确定性人工智能领域，主要侧重于不确定性人工智能的知识表达、获取以及推理等问题。本文整理了2019年大会的接受论文列表，方便读者查阅。
| ID: 1 | Personalized Peer Truth Serum for Eliciting Multi-Attribute Personal Data |
| Naman Goel, Boi Faltings | |
| ID: 6 | Conditional Expectation Propagation |
| Zheng Wang, Shandian Zhe | |
| ID: 7 | A Sparse Representation-Based Approach to Linear Regression with Partially Shuffled Labels |
| Martin Slawski, Mostafa Rahmani, Ping Li | |
| ID: 13 | On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don’t Worry About its Nonsmooth Loss Function |
| Xingguo Li, Haoming Jiang, Jarvis Haupt, Raman Arora, Han Liu, Mingyi Hong, Tuo Zhao | |
| ID: 14 | Correlated Learning for Aggregation Systems |
| Tanvi Verma, Pradeep Varakantham | |
| ID: 15 | Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias |
| Patrick Forré, Joris M. Mooij | |
| ID: 16 | Variational Regret Bounds for Reinforcement Learning |
| Ronald Ortner, Pratik Gajane, Peter Auer | |
| ID: 17 | Recommendation from Raw Data with Adaptive Compound Poisson Factorization |
| Olivier Gouvert, Thomas Oberlin, Cédric Févotte | |
| ID: 19 | One-Shot Inference in Markov Random Fields |
| Hao Xiong, Yuanzhen Guo, Yibo Yang, Nicholas Ruozzi | |
| ID: 21 | Truly Proximal Policy Optimization |
| Yuhui Wang, Hao He, Xiaoyang Tan | |
| ID: 24 | Learning Factored Markov Decision Processes with Unawareness |
| Craig Innes, Alex Lascarides | |
| ID: 25 | Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions |
| Tim Pearce, Russell Tsuchida, Mohamed Zaki, Alexandra Brintrup, Andy Neely | |
| ID: 28 | Countdown Regression: Sharp and Calibrated Survival Predictions |
| Anand Avati, Tony Duan, Sharon Zhou, Ken Jung, Nigam H. Shah, Andrew Ng | |
| ID: 32 | Reducing Exploration of Dying Arms in Mortal Bandits |
| Stefano Tracà, Weiyu Yan, Cynthia Rudin | |
| ID: 33 | Comparing EM with GD in Mixture Models of Two Components |
| Guojun Zhang, Pascal Poupart, George Trimponias | |
| ID: 35 | Efficient Search-Based Weighted Model Integration |
| Zhe Zeng, Guy Van den Broeck | |
| ID: 45 | Causal Discovery with General Non-Linear Relationships using Non-Linear ICA |
| Ricardo Pio Monti, Kun Zhang, Aapo Hyvarinen | |
| ID: 47 | BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback |
| Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvari, Masrour Zoghi | |
| ID: 49 | Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory |
| Philipp Geiger, Michel Besserve, Justus Winkelmann, Claudius Proissl, Bernhard Schoelkopf | |
| ID: 53 | The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA |
| Luigi Gresele, Paul Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schoelkopf | |
| ID: 55 | Random Clique Covers for Graphs with Local Density and Global Sparsity |
| Sinead A. Williamson, Mauricio Tec | |
| ID: 64 | Randomized Iterative Algorithms for Fisher Discriminant Analysis |
| Agniva Chowdhury, Jiasen Yang, Petros Drineas | |
| ID: 78 | Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests |
| Taoan Huang, Bohui Fang, Xiaohui Bei, Fei Fang | |
| ID: 83 | Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation |
| Cong Xie, Oluwasanmi Koyejo, Indranil Gupta | |
| ID: 86 | Adaptive Hashing for Model Counting |
| Jonathan Kuck, Tri Dao, Shenjia Zhao, Burak Bartan, Ashish Sabharwal, Stefano Ermon | |
| ID: 91 | Towards a Better Understanding and Regularization of GAN Training Dynamics |
| Weili Nie, Ankit Patel | |
| ID: 101 | Domain Generalization via Multidomain Discriminant Analysis |
| Shoubo Hu, Kun Zhang, Zhitang Chen, Laiwan Chan | |
| ID: 112 | Efficient Planning Under Uncertainty with Incremental Refinement |
| Juan Carlos Saborío, Joachim Hertzberg | |
| ID: 118 | Cubic Regularization with Momentum for Nonconvex Optimization |
| Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan | |
| ID: 122 | Stability of Linear Structural Equation Models of Causal Inference |
| Karthik Abinav Sankararaman, Anand Louis, Navin Goyal | |
| ID: 124 | 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 | |
| ID: 127 | Towards Robust Relational Causal Discovery |
| Sanghack Lee, Vasant Honavar | |
| ID: 128 | The Role of Memory in Stochastic Optimization |
| Antonio Orvieto, Jonas Kohler, Aurelien Lucchi | |
| ID: 129 | Random Search and Reproducibility for Neural Architecture Search |
| Liam Li, Ameet Talwalkar | |
| ID: 138 | Joint Nonparametric Precision Matrix Estimation with Confounding |
| Sinong Geng, Mladen Kolar, Oluwasanmi Koyejo | |
| ID: 144 | General Identifiability with Arbitrary Surrogate Experiments |
| Sanghack Lee, Juan D. Correa, Elias Bareinboim | |
| ID: 148 | Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem |
| Karen Ullrich, Rianne van den Berg, Marcus A. Brubaker, David Fleet, Max Welling | |
| ID: 152 | Approximate Inference in Structured Instances with Noisy Categorical Observations |
| Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas | |
| ID: 156 | Randomized Value Functions via Multiplicative Normalizing Flows |
| Ahmed Touati, Harsh Satija, Joshua Romoff, Joelle Pineau, Pascal Vincent | |
| ID: 158 | A Fast Proximal Point Method for Computing Exact Wasserstein Distance |
| Yujia Xie, Xiangfeng Wang, Ruijia Wang, Hongyuan Zha | |
| ID: 159 | Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks |
| Qi She, Anqi Wu | |
| ID: 161 | Fisher-Bures Adversary Graph Convolutional Networks |
| Ke Sun, Piotr Koniusz, Zhen Wang | |
| ID: 162 | Epsilon-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning |
| Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee | |
| ID: 163 | Periodic Kernel Approximation by Index Set Fourier Series Features |
| Anthony Tompkins, Fabio Ramos | |
| ID: 164 | Efficient Neural Network Verification with Exactness Characterization |
| Krishnamurthy (Dj) Dvijotham, Robert Stanforth, Sven Gowal, Sven Gowal, Chongli Qin, Soham De, Pushmeet Kohli | |
| ID: 172 | Augmenting and Tuning Knowledge Graph Embeddings |
| Robert Bamler, Farnood Salehi, Stephan Mandt | |
| ID: 174 | A Tighter Analysis of Randomised Policy Iteration |
| Meet Taraviya, Shivaram Kalyanakrishnan | |
| ID: 176 | Perturbed-History Exploration in Stochastic Linear Bandits |
| Branislav Kveton, Csaba Szepesvari, Mohammad Ghavamzadeh, Craig Boutilier | |
| ID: 191 | An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient |
| Pan Xu, Felicia Gao, Quanquan Gu | |
| ID: 192 | 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 | |
| ID: 193 | Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation |
| Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir | |
| ID: 204 | Sliced Score Matching: A Scalable Approach to Density and Score Estimation |
| Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon | |
| ID: 205 | Beyond Structural Causal Models: Causal Constraints Models |
| Tineke Blom, Stephan Bongers, Joris M. Mooij | |
| ID: 206 | Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits |
| Aadirupa Saha, Shreyas Sheshadri, Chiranjib Bhattacharyya | |
| ID: 210 | Approximate Causal Abstractions |
| Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern | |
| ID: 213 | The Sensitivity of Counterfactual Fairness to Unmeasured Confounding |
| Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva | |
| ID: 221 | Belief Propagation: Accurate Marginals or Accurate Partition Function -- Where is the Difference? |
| Christian Knoll, Franz Pernkopf | |
| ID: 222 | Finding Minimal d-separators in Linear Time and Applications |
| Benito van der Zander, Maciej Liśkiewicz | |
| ID: 228 | A Bayesian Approach to Robust Reinforcement Learning |
| Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor | |
| ID: 232 | Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization |
| Guanghui Wang, Shiyin Lu, Lijun Zhang | |
| ID: 234 | Evacuate or Not? A POMDP Model of the Decision Making of Individuals in Hurricane Evacuation Zones |
| Adithya Raam Sankar, Prashant Doshi, Adam Goodie | |
| ID: 235 | 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 | |
| ID: 239 | Variational Sparse Coding |
| Francesco Tonolini, Bjørn Sand Jensen, Roderick Murray-Smith | |
| ID: 244 | Learning with Non-Convex Truncated Losses by SGD |
| Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang | |
| ID: 245 | Active Multi-Information Source Bayesian Quadrature |
| Alexandra Gessner, Javier Gonzalez, Maren Mahsereci | |
| ID: 248 | 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 | |
| ID: 253 | Sinkhorn AutoEncoders |
| Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen | |
| ID: 262 | How to Exploit Structure while Solving Weighted Model Integration Problems |
| Samuel Kolb, Pedro Zuidberg Dos Martires, Luc De Raedt | |
| ID: 264 | Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation |
| Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper | |
| ID: 267 | A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations |
| Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos | |
| ID: 275 | Efficient Multitask Feature and Relationship Learning |
| Han Zhao, Otilia Stretcu, Alexander J. Smola, Geoffrey J. Gordon | |
| ID: 284 | Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning |
| Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier, Andrew Gordon Wilson | |
| ID: 290 | Adaptively Truncating Backpropagation Through Time to Control Gradient Bias |
| Christopher Aicher, Nicholas J. Foti, Emily B. Fox | |
| ID: 296 | Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging |
| Seong Jae Hwang, Ronak R. Mehta, Hyunwoo J. Kim, Sterling C. Johnson, Vikas Singh | |
| ID: 299 | Online Factorization and Partition of Complex Networks by Random Walk |
| Lin F. Yang, Zheng Yu, Vladimir Braverman, Tuo Zhao, Mengdi Wang | |
| ID: 302 | On Densification for Minwise Hashing |
| Tung Mai, Anup Rao, Matt Kapilevich, Ryan Rossi, Yasin Abbsi Yadkori, Ritwik Sinha | |
| ID: 310 | N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification |
| Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee | |
| ID: 312 | Problem-dependent Regret Bounds for Online Learning with Feedback Graphs |
| Bingshan Hu, Nishant A. Mehta, Jianping Pan | |
| ID: 315 | Wasserstein Fair Classification |
| Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa | |
| ID: 317 | Variational Training for Large-Scale Noisy-OR Bayesian Networks |
| Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik Sudderth | |
| ID: 319 | Guaranteed Scalable Learning of Latent Tree Models |
| Furong Huang, Niranjan UN, Ioakeim Perros, Robert Chen, Jimeng Sun, Anima Anandkumar | |
| ID: 324 | On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits |
| Roman Pogodin, Tor Lattimore | |
| ID: 332 | Noise Contrastive Priors for Functional Uncertainty |
| Danijar Hafner, Dustin Tran, Timothy Lillicrap, Alex Irpan, James Davidson | |
| ID: 334 | Fake It Till You Make It: Learning-Compatible Performance ort |
| Jonathan Bragg, Emma Brunskill | |
| ID: 335 | Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning |
| Smitha Milli, Anca D. Dragan | |
| ID: 339 | Convergence Analysis of Gradient-Based Learning in Continuous Games |
| Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Sam Burden | |
| ID: 340 | End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations |
| Gregory Gundersen, Bianca Dumitrascu, Jordan T. Ash, Barbara E. Engelhardt | |
| ID: 341 | Approximate Relative Value Learning for Average-reward Continuous State MDPs |
| Hiteshi Sharma, Mehdi Jafarnia-Jahromi, Rahul Jain | |
| ID: 345 | Exact Sampling of Directed Acyclic Graphs from Modular Distributions |
| Topi Talvitie, Aleksis Vuoksenmaa, Mikko Koivisto | |
| ID: 352 | Intervening on Network Ties |
| Eli Sherman, Ilya Shpitser | |
| ID: 356 | Generating and Sampling Orbits for Lifted Probabilistic Inference |
| Steven Holtzen, Todd Millstein, Guy Van den Broeck | |
| ID: 368 | Real-Time Robotic Search using Structural Spatial Point Processes |
| Olov Andersson, Per Sidén, Johan Dahlin, Patrick Doherty, Mattias Villani | |
| ID: 370 | Social Reinforcement Learning to Combat Fake News Spread |
| Mahak Goindani, Jennifer Neville | |
| ID: 371 | P3O: Policy-on Policy-off Policy Optimization |
| Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola | |
| ID: 372 | Causal Inference Under Interference And Network Uncertainty |
| Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser | |
| ID: 373 | Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow |
| Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood | |
| ID: 380 | Learnability for the Information Bottleneck |
| Tailin Wu, Ian Fischer, Isaac Chuang, Max Tegmark | |
| ID: 383 | Learning Belief Representations for Imitation Learning in POMDPs |
| Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng | |
| ID: 393 | Object Conditioning for Causal Inference |
| David Jensen, Javier Burroni, Matthew Rattigan | |
| ID: 403 | CCMI : Classifier based Conditional Mutual Information Estimation |
| Sudipto Mukherjee, Himanshu Asnani, Sreeram Kannan | |
| ID: 406 | Empirical Mechanism Design: Designing Mechanisms from Data |
| Enrique Areyan Viqueira, Cyrus Cousins, Yasser Mohammad, Amy Greenwald | |
| ID: 407 | On the Relationship Between Satisfiability and Markov Decision Processes |
| Ricardo Salmon, Pascal Poupart | |
| ID: 410 | Interpretable Almost Matching Exactly With Instrumental Variables |
| M.Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky | |
| ID: 411 | Low Frequency Adversarial Perturbation |
| Chuan Guo, Jared S. Frank, Kilian Q. Weinberger | |
| ID: 427 | Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption |
| Ondrej Kuzelka, Jesse Davis | |
| ID: 428 | Identification In Missing Data Models Represented By Directed Acyclic Graphs |
| Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins | |
| ID: 432 | A Weighted Mini-Bucket Bound for Solving Influence Diagram |
| Junkyu Lee, Radu Marinescu, Alexander Iher, Rina Dechter | |
| ID: 435 | Subspace Inference for Bayesian Deep Learning |
| Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson | |
| ID: 440 | Off-Policy Policy Gradient with Stationary Distribution Correction |
| Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill | |
| ID: 441 | Co-training for Policy Learning |
| Jialin Song, Ravi Lanka, Yisong Yue, Masahiro Ono | |
| ID: 443 | Variational Inference of Penalized Regression with Submodular Functions |
| Koh Takeuchi, Yuichi Yoshida, Yoshinobu Kawahara | |
| ID: 450 | Probability Distillation: A Caveat and Alternatives |
| Chin-Wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron Courville | |
| ID: 468 | Bayesian Optimization with Binary Auxiliary Information |
| Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low | |
| ID: 481 | On Open-Universe Causal Reasoning |
| Duligur Ibeling, Thomas Icard | |
| ID: 496 | Embarrassingly Parallel MCMC using Deep Invertible Transformations |
| Diego Mesquita, Paul Blomstedt, Samuel Kaski | |
| ID: 508 | Fast Proximal Gradient Descent for A Class of Non-convex and Non-smooth Sparse Learning Problems |
| Yingzhen Yang, Jiahui Yu | |
| ID: 511 | Block Neural Autoregressive Flow |
| Nicola De Cao, Wilker Aziz, Ivan Titov | |
| ID: 512 | Exclusivity Graph Approach to Instrumental Inequalities |
| Davide Poderini, Rafael Chaves, Iris Agresti, Gonzalo Carvacho, Fabio Sciarrino | |
-END-
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The Conference on Uncertainty in Artificial Intelligence (UAI) is the premier international conference on research related to representation, inference, learning and decision making in the presence of uncertainty within the field of Artificial Intelligence. UAI is supported by the Association for Uncertainty in Artificial Intelligence (AUAI).
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