Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence

Ze Jin, Xiaohan Yan, David S. Matteson; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:1-11

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Analysis of Thompson Sampling for Graphical Bandits Without the Graphs

Fang Liu, Zizhan Zheng, Ness Shroff; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:12-21

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Structured nonlinear variable selection

Magda Gregorova, Alexandros Kalousis, Stephane Marchand-Maillet; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:22-31

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Identification of Strong Edges in AMP Chain Graphs

Jose M. Peña; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:32-41

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A Univariate Bound of Area Under ROC

Siwei Lyu, Yiming Ying; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:42-51

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Efficient Bayesian Inference for a Gaussian Process Density Model

Christian Donner, Manfred Opper; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:52-61

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Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return

Craig Sherstan, Dylan R. Ashley, Brendan Bennett, Kenny Young, Adam White, Martha White, Richard S. Sutton; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:62-71

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How well does your sampler really work?

Ryan Turner, Brady Neal; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:72-81

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Learning Deep Hidden Nonlinear Dynamics from Aggregate Data

Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:82-91

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Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain

Yu-Xiang Wang; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:92-102

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Imaginary Kinematics

Sabina Marchetti, Alessandro Antonucci; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:103-112

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From Deterministic ODEs to Dynamic Structural Causal Models

Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schoelkopf; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:113-122

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Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint

Han Zhao, Geoff Gordon; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:123-133

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Learning Time Series Segmentation Models from Temporally Imprecise Labels

Roy Adams, Benjamin M. Marlin; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:134-143

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Multi-Target Optimisation via Bayesian Optimisation and Linear Programming

Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:144-154

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Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error

Sinong Geng, Zhaobin Kuang, Jie Liu, Stephen Wright, David Page; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:155-165

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Active Information Acquisition for Linear Optimization

Shuran Zheng, Bo Waggoner, Yang Liu, Yiling Chen; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:166-175

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Transferable Meta Learning Across Domains

Bingyi Kang, Jiashi Feng; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:176-186

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Learning the Causal Structure of Copula Models with Latent Variables

Ruifei Cui, Perry Groot, Moritz Schauer, Tom Heskes; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:187-196

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$f_BGD$: Learning Embeddings From Positive Unlabeled Data with BGD

Fajie YUAN, Xin Xin, Xiangnan He, Guibing Guo, Weinan Zhang, CHUA Tat-Seng, Joemon Jose; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:197-206

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Soft-Robust Actor-Critic Policy-Gradient

Esther Derman, Daniel J Mankowitz, Timothy A Mann, Shie Mannor; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:207-217

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Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling

Dmitry Babichev, Francis Bach; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:218-227

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Discrete Sampling using Semigradient-based Product Mixtures

Alkis Gotovos, Hamed Hassani, Andreas Krause, Stefanie Jegelka; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:228-236

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Combining Knowledge and Reasoning through Probabilistic Soft Logic for Image Puzzle Solving

Somak Aditya, Yezhou Yang, Chitta Baral, Yiannis Aloimonos; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:237-247

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Nesting Probabilistic Programs

Tom Rainforth; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:248-257

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Scalable Algorithms for Learning High-Dimensional Linear Mixed Models

Zilong Tan, Kimberly Roche, Xiang Zhou, Sayan Mukherjee; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:258-267

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Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders

Patrick Forré, Joris M. Mooij; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:268-277

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Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models

Tatiana Shpakova, Francis Bach, Anton Osokin; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:278-288

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Variational Inference for Gaussian Processes with Panel Count Data

Hongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:289-298

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A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data

Ricardo Pio Monti, Aapo Hyvarinen; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:299-308

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Improved Stochastic Trace Estimation using Mutually Unbiased Bases

JK Fitzsimons, MA Osborne, SJ Roberts, JF Fitzsimons; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:309-317

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Unsupervised Multi-view Nonlinear Graph Embedding

Jiaming Huang, Zhao Li, Vincent W. Zheng, Wen Wen, Yifan Yang, Yuanmi Chen; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:318-327

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Graph-based Clustering under Differential Privacy

Rafael Pinot, Anne Morvan, Florian Yger, Cedric Gouy-Pailler, Jamal Atif; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:328-337

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GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-yan Yeung; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:338-348

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Causal Learning for Partially Observed Stochastic Dynamical Systems

Søren Wengel Mogensen, Daniel Malinsky, Niels Richard Hansen; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:349-359

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Variational zero-inflated Gaussian processes with sparse kernels

Pashupati Hegde, Markus Heinonen, Samuel Kaski; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:360-370

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KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features

Alberto Garcia-Duran, Mathias Niepert; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:371-380

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Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning

Yuval Atzmon, Gal Chechik; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:381-391

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Sylvester Normalizing Flows for Variational Inference

Rianne van den Berg, Leonard Hasenclever, Jakub Tomczak, Max Welling; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:392-401

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Holistic Representations for Memorization and Inference

Yunpu Ma, Marcel Hildebrandt, Volker Tresp, Stephan Baier; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:402-412

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Simple and practical algorithms for $\ell_p$-norm low-rank approximation

Anastasios Kyrillidis; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:413-423

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Quantile-Regret Minimisation in Infinitely Many-Armed Bandits

Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:424-433

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Variational Inference for Gaussian Process Models for Survival Analysis

Minyoung Kim, Vladimir Pavlovic; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:434-444

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A Cost-Effective Framework for Preference Elicitation and Aggregation

Zhibing Zhao, Haoming Li, Junming Wang, Jeffrey O. Kephart, Nicholas Mattei, Hui Su, Lirong Xia; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:445-455

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Incremental Learning-to-Learn with Statistical Guarantees

Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:456-465

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Bandits with Side Observations: Bounded vs. Logarithmic Regret

Rémy Degenne, Evrard Garcelon, Vianney Perchet; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:466-475

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Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks

Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:476-485

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Clustered Fused Graphical Lasso

Yizhi Zhu, Oluwasanmi Koyejo; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:486-495

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Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks

David Zheng, Vinson Luo, Jiajun Wu, Joshua Tenenbaum; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:496-506

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Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics

Difan Zou, Pan Xu, Quanquan Gu; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:507-517

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Finite-State Controllers of POMDPs using Parameter Synthesis

Sebastian Junges, Nils Jansen, Ralf Wimmer, Tim Quatmann, Leonore Winterer, Joost-Pieter Katoen, Bernd Becker; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:518-528

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Identification of Personalized Effects Associated With Causal Pathways

Ilya Shpitser, Eli Sherman; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:529-538

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Fast Counting in Machine Learning Applications

Subhadeep Karan, Matthew Eichhorn, Blake Hurlburt, Grant Iraci, Jaroslaw Zola; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:539-548

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A Dual Approach to Scalable Verification of Deep Networks

Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, Pushmeet Kohli; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:549-558

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Understanding Measures of Uncertainty for Adversarial Example Detection

Lewis Smith, Yarin Gal; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:559-568

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Causal Discovery in the Presence of Measurement Error

Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:569-578

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IDK Cascades: Fast Deep Learning by Learning not to Overthink

Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:579-589

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Learning Fast Optimizers for Contextual Stochastic Integer Programs

Vinod Nair, Dj Dvijotham, Iain Dunning, Oriol Vinyals; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:590-599

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Differential Analysis of Directed Networks

Min Ren, Dabao Zhang; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:600-609

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Sparse-Matrix Belief Propagation

Reid Bixler, Bert Huang; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:610-619

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Sequential Learning under Probabilistic Constraints

Amirhossein Meisami, Henry Lam, Chen Dong, Abhishek Pani; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:620-630

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Abstraction Sampling in Graphical Models

Filjor Broka, Rina Dechter, Alexander Ihler, Kalev Kask; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:631-640

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Meta Reinforcement Learning with Latent Variable Gaussian Processes

Steindor Saemundsson, Katja Hofmann, Marc Peter Deisenroth; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:641-651

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Non-Parametric Path Analysis in Structural Causal Models

Junzhe Zhang, Elias Bareinboim; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:652-661

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Stochastic Layer-Wise Precision in Deep Neural Networks

Griffin Lacey, Graham W. Taylor, Shawki Areibi; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:662-671

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Estimation of Personalized Effects Associated With Causal Pathways

Razieh Nabi, Phyllis Kanki, Ilya Shpitser; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:672-681

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High-confidence error estimates for learned value functions

Touqir Sajed, Wesley Chung, Martha White; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:682-691

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Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences

Tanner Fiez, Shreyas Sekar, Liyuan Zheng, Lillian Ratliff; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:692-702

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Sparse Multi-Prototype Classification

Vikas K. Garg, Lin Xiao, Ofer Dekel; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:703-713

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Fast Stochastic Quadrature for Approximate Maximum-Likelihood Estimation

Nico Piatkowski, Katharina Morik; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:714-723

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Finite-sample Bounds for Marginal MAP

Qi Lou, Rina Dechter, Alexander Ihler; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:724-733

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Acyclic Linear SEMs Obey the Nested Markov Property

Ilya Shpitser, Robin Evans, Thomas S. Richardson; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:734-744

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A Unified Particle-Optimization Framework for Scalable Bayesian Sampling

Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:745-754

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An Efficient Quantile Spatial Scan Statistic for Finding Unusual Regions in Continuous Spatial Data with Covariates

Travis Moore, Weng-Keen Wong; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:755-764

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Stable Gradient Descent

Yingxue Zhou, Sheng Chen, Arindam Banerjee; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:765-774

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Learning to select computations

Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:775-784

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Per-decision Multi-step Temporal Difference Learning with Control Variates

Kristopher De Asis, Richard S. Sutton; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:785-793

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The Indian Buffet Hawkes Process to Model Evolving Latent Influences

Xi Tan, Vinayak Rao, Jennifer Neville; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:794-803

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Battle of Bandits

Aadirupa Saha, Aditya Gopalan; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:804-813

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Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields

Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:814-823

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Adaptive Stratified Sampling for Precision-Recall Estimation

Ashish Sabharwal, Yexiang Xue; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:824-833

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Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes

Cristian Guarnizo, Mauricio Álvarez; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:834-843

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Fast Policy Learning through Imitation and Reinforcement

Ching-An Cheng, Xinyan Yan, Nolan Wagener, Byron Boots; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:844-854

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Hyperspherical Variational Auto-Encoders

Tim Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:855-864

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Dissociation-Based Oblivious Bounds for Weighted Model Counting

Li Chou, Wolfgang Gatterbauer, Vibhav Gogate; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:865-874

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Averaging Weights Leads to Wider Optima and Better Generalization

Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:875-884

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Block-Value Symmetries in Probabilistic Graphical Models

Gagan Madan, Ankit Anand,  Mausam, Parag Singla; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:885-894

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Max-margin learning with the Bayes factor

Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David Sontag; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:895-904

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Densified Winner Take All (WTA) Hashing for Sparse Datasets

Beidi Chen, Anshumali Shrivastava; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:905-915

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Lifted Marginal MAP Inference

Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:916-925

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PAC-Reasoning in Relational Domains

Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:926-935

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Pure Exploration of Multi-Armed Bandits with Heavy-Tailed Payoffs

Xiaotian Yu, Han Shao, Michael R. Lyu, Irwin King; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:936-945

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Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms

Adarsh Subbaswamy, Suchi Saria; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:946-956

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Decentralized Planning for Non-dedicated Agent Teams with Submodular Rewards in Uncertain Environments

Pritee Agrawal, Pradeep Varakantham, William Yeoh; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:957-966

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A Forest Mixture Bound for Block-Free Parallel Inference

Neal Lawton, Greg Ver Steeg, Aram Galstyan; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:967-976

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Causal Identification under Markov Equivalence

Amin Jaber, Jiji Zhang, Elias Bareinboim; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:977-986

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The Variational Homoencoder: Learning to learn high capacity generative models from few examples

Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi Jaakkola, Joshua B. Tenenbaum; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:987-996

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Probabilistic Collaborative Representation Learning for Personalized Item Recommendation

Aghiles Salah, Hady W. Lauw; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:997-1007

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Reforming Generative Autoencoders via Goodness-of-Fit Hypothesis Testing

Aaron Palmer, Dipak Dey, Jinbo Bi; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:1008-1018

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Towards Flatter Loss Surface via Nonmonotonic Learning Rate Scheduling

Sihyeon Seong, Yegang Lee, Youngwook Kee, Dongyoon Han, Junmo Kim; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:1019-1029

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A Lagrangian Perspective on Latent Variable Generative Models

Shengjia Zhao, Jiaming Song, Stefano Ermon; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:1030-1040

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Bayesian optimization and attribute adjustment

Stephan Eismann, Daniel Levy, Rui Shu, Stefan Bartzsch, Stefano Ermon; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:1041-1051

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Join Graph Decomposition Bounds for Influence Diagrams

Junkyu Lee, Alexander Ihler, Rina Dechter; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:1052-1061

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Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results

Kun Zhang, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour; Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:1062-1071

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