default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
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.
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2025-02-09 14:44 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
