UAI 2018

104 papers

A Cost-Effective Framework for Preference Elicitation and Aggregation Zhibing Zhao, Haoming Li, Junming Wang, Jeffrey O. Kephart, Nicholas Mattei, Hui Su, Lirong Xia

A Dual Approach to Scalable Verification of Deep Networks Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy A. Mann, Pushmeet Kohli

A Forest Mixture Bound for Block-Free Parallel Inference Neal Lawton, Greg Ver Steeg, Aram Galstyan

A Lagrangian Perspective on Latent Variable Generative Models Shengjia Zhao, Jiaming Song, Stefano Ermon

A Unified Particle-Optimization Framework for Scalable Bayesian Sampling Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen

A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data Ricardo Pio Monti, Aapo Hyvärinen

A Univariate Bound of Area Under ROC Siwei Lyu, Yiming Ying

Abstraction Sampling in Graphical Models Filjor Broka, Rina Dechter, Alexander Ihler, Kalev Kask

Active Information Acquisition for Linear Optimization Shuran Zheng, Bo Waggoner, Yang Liu, Yiling Chen

Acyclic Linear SEMs Obey the Nested Markov Property Ilya Shpitser, Robin J. Evans, Thomas S. Richardson

Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien

Adaptive Stratified Sampling for Precision-Recall Estimation Ashish Sabharwal, Yexiang Xue

An Efficient Quantile Spatial Scan Statistic for Finding Unusual Regions in Continuous Spatial Data with Covariates Travis Moore, Weng-Keen Wong

Analysis of Thompson Sampling for Graphical Bandits Without the Graphs Fang Liu, Zizhan Zheng, Ness B. Shroff

Averaging Weights Leads to Wider Optima and Better Generalization Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson

Bandits with Side Observations: Bounded vs. Logarithmic Regret Rémy Degenne, Evrard Garcelon, Vianney Perchet

Battle of Bandits Aadirupa Saha, Aditya Gopalan

Bayesian Optimization and Attribute Adjustment Stephan Eismann, Daniel Levy, Rui Shu, Stefan Bartzsch, Stefano Ermon

Block-Value Symmetries in Probabilistic Graphical Models Gagan Madan, Ankit Anand, Mausam, Parag Singla

Causal Discovery in the Presence of Measurement Error Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij

Causal Discovery with Linear Non-Gaussian Models Under Measurement Error: Structural Identifiability Results Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour

Causal Identification Under Markov Equivalence Amin Jaber, Jiji Zhang, Elias Bareinboim

Causal Learning for Partially Observed Stochastic Dynamical Systems Søren Wengel Mogensen, Daniel Malinsky, Niels Richard Hansen

Clustered Fused Graphical Lasso Yizhi Zhu, Oluwasanmi Koyejo

Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences Tanner Fiez, Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff

Combining Knowledge and Reasoning Through Probabilistic Soft Logic for Image Puzzle Solving Somak Aditya, Yezhou Yang, Chitta Baral, Yiannis Aloimonos

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

Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling Dmitry Babichev, Francis R. Bach

Constraint-Based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders Patrick Forré, Joris M. Mooij

Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms Adarsh Subbaswamy, Suchi Saria

Decentralized Planning for Non-Dedicated Agent Teams with Submodular Rewards in Uncertain Environments Pritee Agrawal, Pradeep Varakantham, William Yeoh

Densified Winner Take All (WTA) Hashing for Sparse Datasets Beidi Chen, Anshumali Shrivastava

Differential Analysis of Directed Networks Min Ren, Dabao Zhang

Discrete Sampling Using Semigradient-Based Product Mixtures Alkis Gotovos, S. Hamed Hassani, Andreas Krause, Stefanie Jegelka

Dissociation-Based Oblivious Bounds for Weighted Model Counting Li Chou, Wolfgang Gatterbauer, Vibhav Gogate

Efficient Bayesian Inference for a Gaussian Process Density Model Christian Donner, Manfred Opper

Estimation of Personalized Effects Associated with Causal Pathways Razieh Nabi, Phyllis Kanki, Ilya Shpitser

Fast Counting in Machine Learning Applications Subhadeep Karan, Matthew Eichhorn, Blake Hurlburt, Grant Iraci, Jaroslaw Zola

Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian Processes Cristian Guarnizo, Mauricio A. Álvarez

Fast Policy Learning Through Imitation and Reinforcement Ching-An Cheng, Xinyan Yan, Nolan Wagener, Byron Boots

Fast Stochastic Quadrature for Approximate Maximum-Likelihood Estimation Nico Piatkowski, Katharina Morik

fBGD: Learning Embeddings from Positive Unlabeled Data with BGD Fajie Yuan, Xin Xin, Xiangnan He, Guibing Guo, Weinan Zhang, Tat-Seng Chua, Joemon M. Jose

Finite-Sample Bounds for Marginal MAP Qi Lou, Rina Dechter, Alexander Ihler

Finite-State Controllers of POMDPs Using Parameter Synthesis Sebastian Junges, Nils Jansen, Ralf Wimmer, Tim Quatmann, Leonore Winterer, Joost-Pieter Katoen, Bernd Becker

Frank-Wolfe Optimization for Symmetric-NMF Under Simplicial Constraint Han Zhao, Geoffrey J. Gordon

From Deterministic ODEs to Dynamic Structural Causal Models Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schölkopf

GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung

Graph-Based Clustering Under Differential Privacy Rafael Pinot, Anne Morvan, Florian Yger, Cédric Gouy-Pailler, Jamal Atif

High-Confidence Error Estimates for Learned Value Functions Touqir Sajed, Wesley Chung, Martha White

Holistic Representations for Memorization and Inference Yunpu Ma, Marcel Hildebrandt, Volker Tresp, Stephan Baier

How Well Does Your Sampler Really Work? Ryan Turner, Brady Neal

Hyperspherical Variational Auto-Encoders Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak

Identification of Personalized Effects Associated with Causal Pathways Ilya Shpitser, Eli Sherman

Identification of Strong Edges in AMP Chain Graphs Jose M. Peña

IDK Cascades: Fast Deep Learning by Learning Not to Overthink Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez

Imaginary Kinematics Sabina Marchetti, Alessandro Antonucci

Improved Stochastic Trace Estimation Using Mutually Unbiased Bases Jack K. Fitzsimons, Michael A. Osborne, Stephen J. Roberts, Joseph Francis Fitzsimons

Incremental Learning-to-Learn with Statistical Guarantees Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil

Join Graph Decomposition Bounds for Influence Diagrams Junkyu Lee, Alexander Ihler, Rina Dechter

KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features Alberto García-Durán, Mathias Niepert

Learning Deep Hidden Nonlinear Dynamics from Aggregate Data Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha

Learning Fast Optimizers for Contextual Stochastic Integer Programs Vinod Nair, Dj Dvijotham, Iain Dunning, Oriol Vinyals

Learning the Causal Structure of Copula Models with Latent Variables Ruifei Cui, Perry Groot, Moritz Schauer, Tom Heskes

Learning Time Series Segmentation Models from Temporally Imprecise Labels Roy Adams, Benjamin M. Marlin

Learning to Select Computations Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder

Lifted Marginal MAP Inference Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla

Marginal Weighted Maximum Log-Likelihood for Efficient Learning of Perturb-and-mAP Models Tatiana Shpakova, Francis R. Bach, Anton Osokin

Max-Margin Learning with the Bayes Factor Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag

Meta Reinforcement Learning with Latent Variable Gaussian Processes Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth

Multi-Target Optimisation via Bayesian Optimisation and Linear Programming Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh

Nesting Probabilistic Programs Tom Rainforth

Non-Parametric Path Analysis in Structural Causal Models Junzhe Zhang, Elias Bareinboim

PAC-Reasoning in Relational Domains Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert

Per-Decision Multi-Step Temporal Difference Learning with Control Variates Kristopher De Asis, Richard S. Sutton

Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning Yuval Atzmon, Gal Chechik

Probabilistic Collaborative Representation Learning for Personalized Item Recommendation Aghiles Salah, Hady W. Lauw

Pure Exploration of Multi-Armed Bandits with Heavy-Tailed Payoffs Xiaotian Yu, Han Shao, Michael R. Lyu, Irwin King

Quantile-Regret Minimisation in Infinitely Many-Armed Bandits Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan

Reforming Generative Autoencoders via Goodness-of-Fit Hypothesis Testing Aaron Palmer, Dipak K. Dey, Jinbo Bi

Revisiting Differentially Private Linear Regression: Optimal and Adaptive Prediction & Estimation in Unbounded Domain Yu-Xiang Wang

Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh

Scalable Algorithms for Learning High-Dimensional Linear Mixed Models Zilong Tan, Kimberly Roche, Xiang Zhou, Sayan Mukherjee

Sequential Learning Under Probabilistic Constraints Amirhossein Meisami, Henry Lam, Chen Dong, Abhishek Pani

Simple and Practical Algorithms for 𝓁p-Norm Low-Rank Approximation Anastasios Kyrillidis

Soft-Robust Actor-Critic Policy-Gradient Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor

Sparse Multi-Prototype Classification Vikas K. Garg, Lin Xiao, Ofer Dekel

Sparse-Matrix Belief Propagation Reid Bixler, Bert Huang

Stable Gradient Descent Yingxue Zhou, Sheng Chen, Arindam Banerjee

Stochastic Layer-Wise Precision in Deep Neural Networks Griffin Lacey, Graham W. Taylor, Shawki Areibi

Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error Sinong Geng, Zhaobin Kuang, Jie Liu, Stephen J. Wright, David Page

Structured Nonlinear Variable Selection Magda Gregorova, Alexandros Kalousis, Stéphane Marchand-Maillet

Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics Difan Zou, Pan Xu, Quanquan Gu

Sylvester Normalizing Flows for Variational Inference Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling

Testing for Conditional Mean Independence with Covariates Through Martingale Difference Divergence Ze Jin, Xiaohan Yan, David S. Matteson

The Indian Buffet Hawkes Process to Model Evolving Latent Influences Xi Tan, Vinayak A. Rao, Jennifer Neville

The Variational Homoencoder: Learning to Learn High Capacity Generative Models from Few Examples Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi S. Jaakkola, Joshua B. Tenenbaum

Towards Flatter Loss Surface via Nonmonotonic Learning Rate Scheduling Sihyeon Seong, Yegang Lee, Youngwook Kee, Dongyoon Han, Junmo Kim

Transferable Meta Learning Across Domains Bingyi Kang, Jiashi Feng

Understanding Measures of Uncertainty for Adversarial Example Detection Lewis Smith, Yarin Gal

Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks David Zheng, Vinson Luo, Jiajun Wu, Joshua B. Tenenbaum

Unsupervised Multi-View Nonlinear Graph Embedding Jiaming Huang, Zhao Li, Vincent W. Zheng, Wen Wen, Yifan Yang, Yuanmi Chen

Variational Inference for Gaussian Process Models for Survival Analysis Minyoung Kim, Vladimir Pavlovic

Variational Inference for Gaussian Processes with Panel Count Data Hongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama

Variational Zero-Inflated Gaussian Processes with Sparse Kernels Pashupati Hegde, Markus Heinonen, Samuel Kaski