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ORCID33rd UAI 2017: Sydney, Australia
- Gal Elidan, Kristian Kersting, Alexander Ihler:
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, August 11-15, 2017. AUAI Press 2017
Keynote Talk
- Leslie Pack Kaelbling:
Intelligent Robots in an Uncertain World. UAI 2017
Session 1: Deep Models
- Ming Jin, Andreas C. Damianou, Pieter Abbeel, Costas J. Spanos:
Inverse Reinforcement Learning via Deep Gaussian Process. - Martin A. Zinkevich, Alex Davies, Dale Schuurmans:
Holographic Feature Representations of Deep Networks. - Gintare Karolina Dziugaite, Daniel M. Roy:
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data.
Session 2: Machine Learning
- U. N. Niranjan, Arun Rajkumar, Theja Tulabandhula:
Provable Inductive Robust PCA via Iterative Hard Thresholding. - Pengtao Xie, Barnabás Póczos, Eric P. Xing:
Near-Orthogonality Regularization in Kernel Methods. - Ashish Sabharwal, Hanie Sedghi:
How Good Are My Predictions? Efficiently Approximating Precision-Recall Curves for Massive Datasets.
Keynote Talk
- Amir Globerson:
Learning and Inference with Expectations. UAI 2017
Session 3: Inference
- Christian Knoll, Franz Pernkopf:
On Loopy Belief Propagation - Local Stability Analysis for Non-Vanishing Fields. - Junyao Zhao, Josip Djolonga, Sebastian Tschiatschek
, Andreas Krause:
Improving Optimization-Based Approximate Inference by Clamping Variables. - Diarmaid Conaty, Cassio P. de Campos, Denis Deratani Mauá:
Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks.
Session 4: Learning
- Yitao Liang, Jessa Bekker, Guy Van den Broeck:
Learning the Structure of Probabilistic Sentential Decision Diagrams. - Abhilash Gaure, Aishwarya Gupta, Vinay Kumar Verma, Piyush Rai:
A Probabilistic Framework for Multi-Label Learning with Unseen Labels. - Volodymyr Kuleshov, Stefano Ermon:
Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning.
Poster Spotlights 1
- Lorenzo Bisi, Giuseppe De Nittis, Francesco Trovò, Marcello Restelli, Nicola Gatti:
Regret Minimization Algorithms for the Followers Behaviour Identification in Leadership Games. - Andrea Celli, Alberto Marchesi, Nicola Gatti:
On the Complexity of Nash Equilibrium Reoptimization. - Zhan Wei Lim, David Hsu, Wee Sun Lee:
Shortest Path under Uncertainty: Exploration versus Exploitation. - Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang:
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels. - Shahaf S. Shperberg, Solomon Eyal Shimony, Ariel Felner:
Monte-Carlo Tree Search using Batch Value of Perfect Information. - Lin Chen, Forrest W. Crawford, Amin Karbasi:
Submodular Variational Inference for Network Reconstruction. - Jack K. Fitzsimons, Kurt Cutajar, Maurizio Filippone, Michael A. Osborne, Stephen J. Roberts:
Bayesian Inference of Log Determinants. - Stephen Mussmann, Daniel Levy, Stefano Ermon:
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. - Tu Dinh Nguyen, Dinh Q. Phung, Viet Huynh, Trung Le:
Supervised Restricted Boltzmann Machines. - Martin Trapp, Tamas Madl, Robert Peharz, Franz Pernkopf, Robert Trappl:
Safe Semi-Supervised Learning of Sum-Product Networks. - Yu Wang, Bin Dai, Gang Hua, John A. D. Aston, David P. Wipf:
Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders. - Van Nguyen:
Approximate Evidential Reasoning Using Local Conditioning and Conditional Belief Functions. - Joonas Jälkö, Antti Honkela, Onur Dikmen:
Differentially Private Variational Inference for Non-conjugate Models. - Bence Cserna, Marek Petrik, Reazul Hasan Russel, Wheeler Ruml:
Value Directed Exploration in Multi-Armed Bandits with Structured Priors. - Eric T. Nalisnick, Padhraic Smyth:
Learning Approximately Objective Priors. - Yihao Feng, Dilin Wang, Qiang Liu:
Learning to Draw Samples with Amortized Stein Variational Gradient Descent. - Yujia Shen, Arthur Choi, Adnan Darwiche:
A Tractable Probabilistic Model for Subset Selection. - Marco Eigenmann, Preetam Nandy, Marloes H. Maathuis:
Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges. - Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer:
SAT-Based Causal Discovery under Weaker Assumptions. - Yewen Pu, Leslie Pack Kaelbling, Armando Solar-Lezama:
Learning to Acquire Information.
Keynote Talk
- Christopher Ré:
Snorkel: Beyond Hand-labeled Data. UAI 2017
Session 5: Representations
- David Buchman, David Poole:
Why Rules are Complex: Real-Valued Probabilistic Logic Programs are not Fully Expressive. - Neil Dhir, Matthijs Vákár, Matthew Wijers, Andrew Markham, Frank D. Wood:
Interpreting Lion Behaviour as Probabilistic Programs. - Jiasen Yang, Vinayak A. Rao, Jennifer Neville:
Decoupling Homophily and Reciprocity with Latent Space Network Models.
Session 6: Reinforcement Learning
- Benjamin van Niekerk, Andreas C. Damianou, Benjamin Rosman:
Online Constrained Model-based Reinforcement Learning. - Niranjani Prasad, Li-Fang Cheng, Corey Chivers, Michael Draugelis, Barbara E. Engelhardt:
A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units. - Swetasudha Panda, Yevgeniy Vorobeychik:
Near-Optimal Interdiction of Factored MDPs. - Shayan Doroudi, Philip S. Thomas, Emma Brunskill:
Importance Sampling for Fair Policy Selection.
Keynote Talk
- Katherine A. Heller:
Machine Learning for Healthcare Data. UAI 2017
Poster Spotlights 2
- Xixian Chen, Irwin King, Michael R. Lyu:
FROSH: FasteR Online Sketching Hashing. - Sanghack Lee, Vasant G. Honavar:
Self-Discrepancy Conditional Independence Test. - Sanghack Lee, Vasant G. Honavar:
Towards Conditional Independence Test for Relational Data. - Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, Maurizio Filippone:
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models. - Zhiqiang Xu, Yiping Ke, Xin Gao:
A Fast Algorithm for Matrix Eigen-decompositionn. - Joe Suzuki, Jun Kawahara:
Branch and Bound for Regular Bayesian Network Structure Learing. - Yangchen Pan, Erfan Sadeqi Azer, Martha White:
Effective sketching methods for value function approximation. - Renbo Zhao, William B. Haskell, Vincent Y. F. Tan:
Stochastic L-BFGS Revisited: Improved Convergence Rates and Practical Acceleration Strategies. - Matt Barnes, Artur Dubrawski:
The Binomial Block Bootstrap Estimator for Evaluating Loss on Dependent Clusters. - Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf:
Data-Dependent Sparsity for Subspace Clustering. - Vaishak Belle:
Weighted Model Counting With Function Symbols. - Xiang Li, Bin Gu, Shuang Ao, Huaimin Wang, Charles X. Ling:
Triply Stochastic Gradients on Multiple Kernel Learning. - Lukas Balles, Javier Romero, Philipp Hennig:
Coupling Adaptive Batch Sizes with Learning Rates. - Robert Zinkov, Chung-chieh Shan:
Composing Inference Algorithms as Program Transformations. - Abdelkader Ouali, David Allouche, Simon de Givry, Samir Loudni, Yahia Lebbah, Lakhdar Loukil:
Iterative Decomposition Guided Variable Neighborhood Search for Graphical Model Energy Minimization. - Yang Liu:
Fair Optimal Stopping Policy for Matching with Mediator. - Rodrigo de Salvo Braz, Ciaran O'Reilly:
Exact Inference for Relational Graphical Models with Interpreted Functions: Lifted Probabilistic Inference Modulo Theories. - Yingzhen Yang, Jiashi Feng, Jiahui Yu, Jianchao Yang, Thomas S. Huang:
Neighborhood Regularized l^1-Graph. - Qinyi Zhang, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic:
Feature-to-Feature Regression for a Two-Step Conditional Independence Test. - Thijs van Ommen, Joris M. Mooij:
Algebraic Equivalence Class Selection for Linear Structural Equation Models.
Keynote Talk
- Terry Speed:
Two current analysis challenges: Single Cell Omics and Nanopore Long-read Sequence Data. UAI 2017
Session 7: Causality
- Hossein Soleimani, Adarsh Subbaswamy, Suchi Saria:
Learning Treatment-Response Models from Multivariate Longitudinal Data. - Emilija Perkovic, Markus Kalisch, Marloes H. Maathuis:
Interpreting and Using CPDAGs With Background Knowledge. - Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf:
Causal Consistency of Structural Equation Models.
Session 8: Sampling
- Jun Han, Qiang Liu:
Stein Variational Adaptive Importance Sampling. - Matthew M. Graham, Amos J. Storkey:
Continuously Tempered Hamiltonian Monte Carlo. - Cheng Zhang, Hedvig Kjellström, Stephan Mandt:
Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes. - Daniel Seita, Xinlei Pan, Haoyu Chen, John F. Canny:
An Efficient Minibatch Acceptance Test for Metropolis-Hastings.
Session 9: Bandits
- Claire Vernade, Olivier Cappé, Vianney Perchet:
Stochastic Bandit Models for Delayed Conversions. - Adam N. Elmachtoub, Ryan McNellis, Sechan Oh, Marek Petrik:
A Practical Method for Solving Contextual Bandit Problems Using Decision Trees. - Aritra Chatterjee, Ganesh Ghalme, Shweta Jain, Rohit Vaish, Y. Narahari:
Analysis of Thompson Sampling for Stochastic Sleeping Bandits.
Poster Spotlights 3
- Chunlai Zhou, Fabio Cuzzolin:
The Total Belief Theorem. - Hugo Gilbert, Olivier Spanjaard:
Complexity of Solving Decision Trees with Skew-Symmetric Bilinear Utility. - Jake Snell, Richard S. Zemel:
Stochastic Segmentation Trees for Multiple Ground Truths. - Yuxin Chen, Jean-Michel Renders, Morteza Haghir Chehreghani, Andreas Krause:
Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting. - Adityanarayanan Radhakrishnan, Liam Solus, Caroline Uhler:
Counting Markov Equivalence Classes by Number of Immoralities. - Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Henri Bouma:
Real-Time Resource Allocation for Tracking Systems. - Manuel Luque, Manuel Arias, Francisco Javier Díez:
Synthesis of Strategies in Influence Diagrams. - Byungkon Kang, Kyung-Ah Sohn:
Embedding Senses via Dictionary Bootstrapping. - Joseph Sakaya, Arto Klami:
Importance Sampled Stochastic Optimization for Variational Inference. - Yanan Sui, Vincent Zhuang, Joel W. Burdick, Yisong Yue:
Multi-dueling Bandits with Dependent Arms. - Junming Yin, Yaoliang Yu:
Convex-constrained Sparse Additive Modeling and Its Extensions. - Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng:
Stein Variational Policy Gradient. - Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, Dacheng Tao:
Causal Discovery from Temporally Aggregated Time Series. - Felipe W. Trevizan, Florent Teichteil-Königsbuch, Sylvie Thiébaux:
Efficient solutions for Stochastic Shortest Path Problems with Dead Ends. - Steven Holtzen, Todd D. Millstein, Guy Van den Broeck:
Probabilistic Program Abstractions. - Yaodong Yu, Sulin Liu, Sinno Jialin Pan:
Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problem. - Muthukumaran Chandrasekaran, Junhuan Zhang, Prashant Doshi, Yifeng Zeng:
Robust Model Equivalence using Stochastic Bisimulation for N-Agent Interactive DIDs. - Pasquale Minervini, Thomas Demeester, Tim Rocktäschel, Sebastian Riedel:
Adversarial Sets for Regularising Neural Link Predictors.
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