Table of Contents

• Testing for Conditional Mean Independence with Covariates through Martingale Difference DivergenceZe Jin, Xiaohan Yan, David S. Matteson. 1-12 [doi]
• Analysis of Thompson Sampling for Graphical Bandits Without the GraphsFang Liu, Zizhan Zheng, Ness B. Shroff. 13-22 [doi]
• Structured nonlinear variable selectionMagda Gregorova, Alexandros Kalousis, Stéphane Marchand-Maillet. 23-32 [doi]
• Identification of Strong Edges in AMP Chain GraphsJose M. Peña. 33-42 [doi]
• A Univariate Bound of Area Under ROCSiwei Lyu, Yiming Ying. 43-52 [doi]
• Efficient Bayesian Inference for a Gaussian Process Density ModelChristian Donner, Manfred Opper. 53-62 [doi]
• Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the ReturnCraig Sherstan, Dylan R. Ashley, Brendan Bennett, Kenny Young, Adam White, Martha White, Richard S. Sutton. 63-72 [doi]
• How well does your sampler really work?Ryan Turner, Brady Neal. 73-82 [doi]
• Learning Deep Hidden Nonlinear Dynamics from Aggregate DataYisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha. 83-92 [doi]
• Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domainYu-Xiang Wang. 93-103 [doi]
• Imaginary KinematicsSabina Marchetti, Alessandro Antonucci. 104-113 [doi]
• From Deterministic ODEs to Dynamic Structural Causal ModelsPaul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schölkopf. 114-123 [doi]
• Frank-Wolfe Optimization for Symmetric-NMF under Simplicial ConstraintHan Zhao, Geoffrey J. Gordon. 124-134 [doi]
• Learning Time Series Segmentation Models from Temporally Imprecise LabelsRoy Adams, Benjamin M. Marlin. 135-144 [doi]
• Multi-Target Optimisation via Bayesian Optimisation and Linear ProgrammingAlistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh. 145-155 [doi]
• Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation ErrorSinong Geng, Zhaobin Kuang, Jie Liu, Stephen Wright, David Page. 156-166 [doi]
• Active Information Acquisition for Linear OptimizationShuran Zheng, Bo Waggoner, Yang Liu, Yiling Chen. 167-176 [doi]
• Transferable Meta Learning Across DomainsBingyi Kang, Jiashi Feng. 177-187 [doi]
• Learning the Causal Structure of Copula Models with Latent VariablesRuifei Cui, Perry Groot, Moritz Schauer, Tom Heskes. 188-197 [doi]
• BGD: Learning Embeddings From Positive Unlabeled Data with BGDFajie Yuan, Xin Xin, Xiangnan He 0001, Guibing Guo, Weinan Zhang, Tat-Seng Chua, Jose M. Joemon. 198-207 [doi]
• Soft-Robust Actor-Critic Policy-GradientEsther Derman, Daniel J. Mankowitz, Timothy Arthur Mann, Shie Mannor. 208-218 [doi]
• Constant Step Size Stochastic Gradient Descent for Probabilistic ModelingDmitry Babichev, Francis Bach. 219-228 [doi]
• Discrete Sampling using Semigradient-based Product MixturesAlkis Gotovos, S. Hamed Hassani, Andreas Krause 0001, Stefanie Jegelka. 229-237 [doi]
• Combining Knowledge and Reasoning through Probabilistic Soft Logic for Image Puzzle SolvingSomak Aditya, Yezhou Yang, Chitta Baral, Yiannis Aloimonos. 238-248 [doi]
• Nesting Probabilistic ProgramsTom Rainforth. 249-258 [doi]
• Scalable Algorithms for Learning High-Dimensional Linear Mixed ModelsZilong Tan, Kimberly Roche, Xiang Zhou, Sayan Mukherjee. 259-268 [doi]
• Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent ConfoundersPatrick Forré, Joris M. Mooij. 269-278 [doi]
• Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map modelsTatiana Shpakova, Francis Bach, Anton Osokin. 279-289 [doi]
• Variational Inference for Gaussian Processes with Panel Count DataHongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama. 290-299 [doi]
• A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data Ricardo Pio Monti, Aapo Hyvärinen. 300-309 [doi]
• Improved Stochastic Trace Estimation using Mutually Unbiased BasesJack K. Fitzsimons, Michael A. Osborne, Stephen J. Roberts, Joseph Francis Fitzsimons. 310-318 [doi]
• Unsupervised Multi-view Nonlinear Graph EmbeddingJiaming Huang, Zhao Li, Vincent W. Zheng, Wen Wen, Yifan Yang, Yuanmi Chen. 319-328 [doi]
• Graph-based Clustering under Differential PrivacyRafael Pinot, Anne Morvan, Florian Yger, Cédric Gouy-Pailler, Jamal Atif. 329-338 [doi]
• GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal GraphsJiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung. 339-349 [doi]
• Causal Learning for Partially Observed Stochastic Dynamical SystemsSøren Wengel Mogensen, Daniel Malinsky, Niels Richard Hansen. 350-360 [doi]
• Variational zero-inflated Gaussian processes with sparse kernelsPashupati Hegde, Markus Heinonen, Samuel Kaski. 361-371 [doi]
• KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical FeaturesAlberto García-Durán, Mathias Niepert. 372-381 [doi]
• Probabilistic AND-OR Attribute Grouping for Zero-Shot LearningYuval Atzmon, Gal Chechik. 382-392 [doi]
• Sylvester Normalizing Flows for Variational InferenceRianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling. 393-402 [doi]
• Holistic Representations for Memorization and InferenceYunpu Ma, Marcel Hildebrandt, Volker Tresp, Stephan Baier. 403-413 [doi]
• p-norm low-rank approximationAnastasios Kyrillidis. 414-424 [doi]
• Quantile-Regret Minimisation in Infinitely Many-Armed BanditsArghya Roy Chaudhuri, Shivaram Kalyanakrishnan. 425-434 [doi]
• Variational Inference for Gaussian Process Models for Survival AnalysisMinyoung Kim, Vladimir Pavlovic. 435-445 [doi]
• A Cost-Effective Framework for Preference Elicitation and AggregationZhibing Zhao, Haoming Li, Junming Wang, Jeffrey O. Kephart, Nicholas Mattei, Hui Su, Lirong Xia. 446-456 [doi]
• Incremental Learning-to-Learn with Statistical GuaranteesGiulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil. 457-466 [doi]
• Bandits with Side Observations: Bounded vs. Logarithmic RegretRémy Degenne, Evrard Garcelon, Vianney Perchet. 467-476 [doi]
• Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse NetworksBenjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh. 477-486 [doi]
• Clustered Fused Graphical LassoYizhi Zhu, Oluwasanmi Koyejo. 487-496 [doi]
• Unsupervised Learning of Latent Physical Properties Using Perception-Prediction NetworksDavid Zheng, Vinson Luo, Jiajun Wu 0001, Joshua B. Tenenbaum. 497-507 [doi]
• Subsampled Stochastic Variance-Reduced Gradient Langevin DynamicsDifan Zou, Pan Xu, Quanquan Gu. 508-518 [doi]
• Finite-State Controllers of POMDPs using Parameter SynthesisSebastian Junges, Nils Jansen 0001, Ralf Wimmer, Tim Quatmann, Leonore Winterer, Joost-Pieter Katoen, Bernd Becker 0001. 519-529 [doi]
• Identification of Personalized Effects Associated With Causal PathwaysIlya Shpitser, Eli Sherman. 530-539 [doi]
• Fast Counting in Machine Learning ApplicationsSubhadeep Karan, Matthew Eichhorn, Blake Hurlburt, Grant Iraci, Jaroslaw Zola. 540-549 [doi]
• A Dual Approach to Scalable Verification of Deep NetworksKrishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy A. Mann, Pushmeet Kohli. 550-559 [doi]
• Understanding Measures of Uncertainty for Adversarial Example DetectionLewis Smith, Yarin Gal. 560-569 [doi]
• Causal Discovery in the Presence of Measurement ErrorTineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij. 570-579 [doi]
• IDK Cascades: Fast Deep Learning by Learning not to OverthinkXin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez. 580-590 [doi]
• Learning Fast Optimizers for Contextual Stochastic Integer ProgramsVinod Nair, Dj Dvijotham, Iain Dunning, Oriol Vinyals. 591-600 [doi]
• Differential Analysis of Directed NetworksMin Ren, Dabao Zhang. 601-610 [doi]
• Sparse-Matrix Belief PropagationReid Bixler, Bert Huang. 611-620 [doi]
• Sequential Learning under Probabilistic ConstraintsAmirhossein Meisami, Henry Lam, Chen Dong, Abhishek Pani. 621-631 [doi]
• Abstraction Sampling in Graphical ModelsFiljor Broka, Rina Dechter, Alexander T. Ihler, Kalev Kask. 632-641 [doi]
• Meta Reinforcement Learning with Latent Variable Gaussian ProcessesSteindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth. 642-652 [doi]
• Non-Parametric Path Analysis in Structural Causal ModelsJunzhe Zhang, Elias Bareinboim. 653-662 [doi]
• Stochastic Layer-Wise Precision in Deep Neural NetworksGriffin Lacey, Graham W. Taylor, Shawki Areibi. 663-672 [doi]
• Estimation of Personalized Effects Associated With Causal PathwaysRazieh Nabi, Phyllis Kanki, Ilya Shpitser. 673-682 [doi]
• High-confidence error estimates for learned value functionsTouqir Sajed, Wesley Chung, Martha White. 683-692 [doi]
• Combinatorial Bandits for Incentivizing Agents with Dynamic PreferencesTanner Fiez, Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff. 693-703 [doi]
• Sparse Multi-Prototype ClassificationVikas K. Garg, Lin Xiao, Ofer Dekel. 704-714 [doi]
• Fast Stochastic Quadrature for Approximate Maximum-Likelihood EstimationNico Piatkowski, Katharina Morik. 715-724 [doi]
• Finite-sample Bounds for Marginal MAPQi Lou, Rina Dechter, Alexander T. Ihler. 725-734 [doi]
• Acyclic Linear SEMs Obey the Nested Markov PropertyIlya Shpitser, Robin J. Evans, Thomas S. Richardson 0001. 735-745 [doi]
• A Unified Particle-Optimization Framework for Scalable Bayesian SamplingChangyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen. 746-755 [doi]
• An Efficient Quantile Spatial Scan Statistic for Finding Unusual Regions in Continuous Spatial Data with CovariatesTravis Moore, Weng-Keen Wong. 756-765 [doi]
• Stable Gradient DescentYingxue Zhou, Sheng Chen, Arindam Banerjee. 766-775 [doi]
• Learning to select computationsFrederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder. 776-785 [doi]
• Per-decision Multi-step Temporal Difference Learning with Control VariatesKristopher De Asis, Richard S. Sutton. 786-794 [doi]
• The Indian Buffet Hawkes Process to Model Evolving Latent InfluencesXi Tan, Vinayak Rao, Jennifer Neville. 795-804 [doi]
• Battle of BanditsAadirupa Saha, Aditya Gopalan. 805-814 [doi]
• Adaptive Stochastic Dual Coordinate Ascent for Conditional Random FieldsRémi Le Priol, Alexandre Piché, Simon Lacoste-Julien. 815-824 [doi]
• Adaptive Stratified Sampling for Precision-Recall EstimationAshish Sabharwal, Yexiang Xue. 825-834 [doi]
• Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCristian Guarnizo, Mauricio A. Álvarez. 835-844 [doi]
• Fast Policy Learning through Imitation and ReinforcementChing-An Cheng, Xinyan Yan, Nolan Wagener, Byron Boots. 845-855 [doi]
• Hyperspherical Variational Auto-EncodersTim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak. 856-865 [doi]
• Dissociation-Based Oblivious Bounds for Weighted Model CountingLi Chou, Wolfgang Gatterbauer, Vibhav Gogate. 866-875 [doi]
• Averaging Weights Leads to Wider Optima and Better GeneralizationPavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson. 876-885 [doi]
• Block-Value Symmetries in Probabilistic Graphical ModelsGagan Madan, Ankit Anand, Mausam, Parag Singla. 886-895 [doi]
• Max-margin learning with the Bayes factorRahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David Sontag. 896-905 [doi]
• Densified Winner Take All (WTA) Hashing for Sparse DatasetsBeidi Chen, Anshumali Shrivastava. 906-916 [doi]
• Lifted Marginal MAP InferenceVishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla. 917-926 [doi]
• PAC-Reasoning in Relational DomainsOndrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert. 927-936 [doi]
• Pure Exploration of Multi-Armed Bandits with Heavy-Tailed PayoffsXiaotian Yu, Han Shao, Michael R. Lyu, Irwin King. 937-946 [doi]
• Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal MechanismsAdarsh Subbaswamy, Suchi Saria. 947-957 [doi]
• Decentralized Planning for Non-dedicated Agent Teams with Submodular Rewards in Uncertain EnvironmentsPritee Agrawal, Pradeep Varakantham, William Yeoh 0001. 958-967 [doi]
• A Forest Mixture Bound for Block-Free Parallel InferenceNeal Lawton, Greg Ver Steeg, Aram Galstyan. 968-977 [doi]
• Causal Identification under Markov EquivalenceAmin Jaber, Jiji Zhang, Elias Bareinboim. 978-987 [doi]
• The Variational Homoencoder: Learning to learn high capacity generative models from few examplesLuke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi S. Jaakkola, Joshua B. Tenenbaum. 988-997 [doi]
• Probabilistic Collaborative Representation Learning for Personalized Item RecommendationAghiles Salah, Hady W. Lauw. 998-1008 [doi]
• Reforming Generative Autoencoders via Goodness-of-Fit Hypothesis TestingAaron Palmer, Dipak Dey, Jinbo Bi. 1009-1019 [doi]
• Towards Flatter Loss Surface via Nonmonotonic Learning Rate SchedulingSihyeon Seong, Yegang Lee, Youngwook Kee, Dongyoon Han, Junmo Kim. 1020-1030 [doi]
• A Lagrangian Perspective on Latent Variable Generative ModelsShengjia Zhao, Jiaming Song, Stefano Ermon. 1031-1041 [doi]
• Bayesian optimization and attribute adjustmentStephan Eismann, Daniel Levy, Rui Shu, Stefan Bartzsch, Stefano Ermon. 1042-1052 [doi]
• Join Graph Decomposition Bounds for Influence DiagramsJunkyu Lee, Alexander T. Ihler, Rina Dechter. 1053-1062 [doi]
• Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability ResultsKun Zhang 0001, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour. 1063-1072 [doi]