UAI 2018大会论文接受列表新鲜出炉

【导读】UAI大会全称为Conference on Uncertainty in Artificial Intelligence,立足于不确定性人工智能领域,主要侧重于不确定性人工智能的知识表达、获取以及推理等问题。本文整理了2018年大会的接受论文列表,方便读者查阅。

详细录用名单日前已经公布,可参见:

http://auai.org/uai2018/accepted.php

ID: 3

Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence

Ze Jin, Xiaohan Yan, David S. Matteson

ID: 14

Analysis of Thompson Sampling for Graphical Bandits Without the Graphs

Fang Liu, Zizhan Zheng, Ness Shroff

ID: 17

Structured nonlinear variable selection

Magda Gregorova, Alexandros Kalousis, Stephane Marchand-Maillet

ID: 23

Identification of Strong Edges in AMP Chain Graphs

Jose M. Peña

ID: 32

A Univariate Bound of Area Under ROC

Siwei Lyu, Yiming Ying

ID: 34

Efficient Bayesian Inference for a Gaussian Process Density Model

Christian Donner, Manfred Opper

ID: 35

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

ID: 37

How well does your sampler really work?

Ryan Turner, Brady Neal

ID: 39

Learning Deep Hidden Nonlinear Dynamics from Aggregate Data

Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha

ID: 40

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

Yu-Xiang Wang

ID: 42

Imaginary Kinematics

Sabina Marchetti, Alessandro Antonucci

ID: 43

From Deterministic ODEs to Dynamic Structural Causal Models

Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schoelkopf

ID: 45

Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint

Han Zhao, Geoff Gordon

ID: 50

Learning Time Series Segmentation Models from Temporally Imprecise Labels

Roy Adams, Benjamin M. Marlin

ID: 53

Multi-Target Optimisation via Bayesian Optimisation and Linear Programming

Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh

ID: 54

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

Sinong Geng, Zhaobin Kuang, Jie Liu, Stephen Wright, David Page

ID: 57

Active Information Acquisition for Linear Optimization

Shuran Zheng, Bo Waggoner, Yang Liu, Yiling Chen

ID: 61

Transferable Meta Learning Across Domains

Bingyi Kang, Jiashi Feng

ID: 65

Learning the Causal Structure of Copula Models with Latent Variables

Ruifei Cui, Perry Groot, Moritz Schauer, Tom Heskes

ID: 68

$f_{BGD}$: Learning Embeddings From Positive Unlabeled Data with BGD

Fajie YUAN, Xin Xin, Xiangnan He, Guibing Guo, Weinan Zhang, CHUA Tat-Seng, Joemon Jose

ID: 70

Soft-Robust Actor-Critic Policy-Gradient

Esther Derman, Daniel J Mankowitz, Timothy A Mann, Shie Mannor

ID: 71

Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling

Dmitry Babichev, Francis Bach

ID: 75

Discrete Sampling using Semigradient-based Product Mixtures

Alkis Gotovos, Hamed Hassani, Andreas Krause, Stefanie Jegelka

ID: 83

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

Somak Aditya, Yezhou Yang, Chitta Baral, Yiannis Aloimonos

ID: 92

Nesting Probabilistic Programs

Tom Rainforth

ID: 99

Scalable Algorithms for Learning High-Dimensional Linear Mixed Models

Zilong Tan, Kimberly Roche, Xiang Zhou, Sayan Mukherjee

ID: 117

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

Patrick Forré, Joris M. Mooij

ID: 118

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

Tatiana Shpakova, Francis Bach, Anton Osokin

ID: 119

Variational Inference for Gaussian Processes with Panel Count Data

Hongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama

ID: 123

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

Ricardo Pio Monti, Aapo Hyvarinen

ID: 125

Improved Stochastic Trace Estimation using Mutually Unbiased Bases

JK Fitzsimons, MA Osborne, SJ Roberts, JF Fitzsimons

ID: 128

Unsupervised Multi-view Nonlinear Graph Embedding

Jiaming Huang, Zhao Li, Vincent W. Zheng, Wen Wen, Yifan Yang, Yuanmi Chen

ID: 132

Graph-based Clustering under Differential Privacy

Rafael Pinot, Anne Morvan, Florian Yger, Cedric Gouy-Pailler, Jamal Atif

ID: 139

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

Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-yan Yeung

ID: 142

Causal Learning for Partially Observed Stochastic Dynamical Systems

Søren Wengel Mogensen, Daniel Malinsky, Niels Richard Hansen

ID: 148

Variational zero-inflated Gaussian processes with sparse kernels

Pashupati Hegde, Markus Heinonen, Samuel Kaski

ID: 149

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

Alberto Garcia-Duran, Mathias Niepert

ID: 151

Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning

Yuval Atzmon, Gal Chechik

ID: 156

Sylvester Normalizing Flows for Variational Inference

Rianne van den Berg, Leonard Hasenclever, Jakub Tomczak, Max Welling

ID: 163

Holistic Representations for Memorization and Inference

Yunpu Ma, Marcel Hildebrandt, Volker Tresp, Stephan Baier

ID: 167

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

Anastasios Kyrillidis

ID: 169

Quantile-Regret Minimisation in Infinitely Many-Armed Bandits

Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan

ID: 171

Variational Inference for Gaussian Process Models for Survival Analysis

Minyoung Kim, Vladimir Pavlovic

ID: 179

A Cost-Effective Framework for Preference Elicitation and Aggregation

Zhibing Zhao, Haoming Li, Junming Wang, Jeffrey O. Kephart, Nicholas Mattei, Hui Su, Lirong Xia

ID: 181

Incremental Learning-to-Learn with Statistical Guarantees

Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil

ID: 182

Bandits with Side Observations: Bounded vs. Logarithmic Regret

Rémy Degenne, Evrard Garcelon, Vianney Perchet

ID: 185

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

Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh

ID: 186

Clustered Fused Graphical Lasso

Yizhi Zhu, Oluwasanmi Koyejo

ID: 191

Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks

David Zheng, Vinson Luo, Jiajun Wu, Joshua Tenenbaum

ID: 192

Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics

Difan Zou, Pan Xu, Quanquan Gu

ID: 195

Finite-State Controllers of POMDPs using Parameter Synthesis

Sebastian Junges, Nils Jansen, Ralf Wimmer, Tim Quatmann, Leonore Winterer, Joost-Pieter Katoen, Bernd Becker

ID: 198

Identification of Personalized Effects Associated With Causal Pathways

Ilya Shpitser, Eli Sherman

ID: 201

Fast Counting in Machine Learning Applications

Subhadeep Karan, Matthew Eichhorn, Blake Hurlburt, Grant Iraci, Jaroslaw Zola

ID: 204

A Dual Approach to Scalable Verification of Deep Networks

Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, Pushmeet Kohli

ID: 207

Understanding Measures of Uncertainty for Adversarial Example Detection

Lewis Smith, Yarin Gal

ID: 208

Causal Discovery in the Presence of Measurement Error

Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij

ID: 212

IDK Cascades: Fast Deep Learning by Learning not to Overthink

Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez

ID: 217

Learning Fast Optimizers for Contextual Stochastic Integer Programs

Vinod Nair, Dj Dvijotham, Iain Dunning, Oriol Vinyals

ID: 221

Differential Analysis of Directed Networks

Min Ren, Dabao Zhang

ID: 225

Sparse-Matrix Belief Propagation

Reid Bixler, Bert Huang

ID: 233

Sequential Learning under Probabilistic Constraints

Amirhossein Meisami, Henry Lam, Chen Dong, Abhishek Pani

ID: 234

Abstraction Sampling in Graphical Models

Filjor Broka, Rina Dechter, Alexander Ihler, Kalev Kask

ID: 235

Meta Reinforcement Learning with Latent Variable Gaussian Processes

Steindor Saemundsson, Katja Hofmann, Marc Peter Deisenroth

ID: 236

Non-Parametric Path Analysis in Structural Causal Models

Junzhe Zhang, Elias Bareinboim

ID: 238

Stochastic Layer-Wise Precision in Deep Neural Networks

Griffin Lacey, Graham W. Taylor, Shawki Areibi

ID: 239

Estimation of Personalized Effects Associated With Causal Pathways

Razieh Nabi, Phyllis Kanki, Ilya Shpitser

ID: 245

High-confidence error estimates for learned value functions

Touqir Sajed, Wesley Chung, Martha White

ID: 247

Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences

Tanner Fiez, Shreyas Sekar, Liyuan Zheng, Lillian Ratliff

ID: 250

Sparse Multi-Prototype Classification

Vikas K. Garg, Lin Xiao, Ofer Dekel

ID: 252

Fast Stochastic Quadrature for Approximate Maximum-Likelihood Estimation

Nico Piatkowski, Katharina Morik

ID: 253

Finite-sample Bounds for Marginal MAP

Qi Lou, Rina Dechter, Alexander Ihler

ID: 255

Acyclic Linear SEMs Obey the Nested Markov Property

Ilya Shpitser, Robin Evans, Thomas S. Richardson

ID: 263

A Unified Particle-Optimization Framework for Scalable Bayesian Sampling

Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen

ID: 265

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

Travis Moore, Weng-Keen Wong

ID: 268

Stable Gradient Descent

Yingxue Zhou, Sheng Chen, Arindam Banerjee

ID: 269

Learning to select computations

Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder

ID: 282

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

Kristopher De Asis, Richard S. Sutton

ID: 289

The Indian Buffet Hawkes Process to Model Evolving Latent Influences

Xi Tan, Vinayak Rao, Jennifer Neville

ID: 290

Battle of Bandits

Aadirupa Saha, Aditya Gopalan

ID: 291

Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields

Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien

ID: 292

Adaptive Stratified Sampling for Precision-Recall Estimation

Ashish Sabharwal, Yexiang Xue

ID: 295

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

Cristian Guarnizo, Mauricio Álvarez

ID: 302

Fast Policy Learning through Imitation and Reinforcement

Ching-An Cheng, Xinyan Yan, Nolan Wagener, Byron Boots

ID: 309

Hyperspherical Variational Auto-Encoders

Tim Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak

ID: 312

Dissociation-Based Oblivious Bounds for Weighted Model Counting

Li Chou, Wolfgang Gatterbauer, Vibhav Gogate

ID: 313

Averaging Weights Leads to Wider Optima and Better Generalization

Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson

ID: 317

Block-Value Symmetries in Probabilistic Graphical Models

Gagan Madan, Ankit Anand, Mausam, Parag Singla

ID: 320

Max-margin learning with the Bayes factor

Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David Sontag

ID: 321

Densified Winner Take All (WTA) Hashing for Sparse Datasets

Beidi Chen, Anshumali Shrivastava

ID: 322

Lifted Marginal MAP Inference

Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla

ID: 325

PAC-Reasoning in Relational Domains

Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert

ID: 332

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

Xiaotian Yu, Han Shao, Michael R. Lyu, Irwin King

ID: 334

Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms

Adarsh Subbaswamy, Suchi Saria

ID: 342

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

Pritee Agrawal, Pradeep Varakantham, William Yeoh

ID: 343

A Forest Mixture Bound for Block-Free Parallel Inference

Neal Lawton, Greg Ver Steeg, Aram Galstyan

ID: 346

Causal Identification under Markov Equivalence

Amin Jaber, Jiji Zhang, Elias Bareinboim

ID: 351

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

ID: 354

Probabilistic Collaborative Representation Learning for Personalized Item Recommendation

Aghiles Salah, Hady W. Lauw

ID: 356

Reforming Generative Autoencoders via Goodness-of-Fit Hypothesis Testing

Aaron Palmer, Dipak Dey, Jinbo Bi

ID: 359

Towards Flatter Loss Surface via Nonmonotonic Learning Rate Scheduling

Sihyeon Seong, Yegang Lee, Youngwook Kee, Dongyoon Han, Junmo Kim

ID: 361

A Lagrangian Perspective on Latent Variable Generative Models

Shengjia Zhao, Jiaming Song, Stefano Ermon

ID: 362

Bayesian optimization and attribute adjustment

Stephan Eismann, Daniel Levy, Rui Shu, Stefan Bartzsch, Stefano Ermon

ID: 367

Join Graph Decomposition Bounds for Influence Diagrams

Junkyu Lee, Alexander Ihler, Rina Dechter

ID: 372

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

-END-

原文发布于微信公众号 - 专知(Quan_Zhuanzhi)

原文发表时间:2018-07-18

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