AISTATS 2019

360 papers

Data-Driven Approach to Multiple-Source Domain Adaptation

Petar Stojanov, Mingming Gong, Jaime Carbonell, Kun Zhang

On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative

Alexander D’Amour

Towards Gradient Free and Projection Free Stochastic Optimization

Anit Kumar Sahu, Manzil Zaheer, Soummya Kar

Evaluating model calibration in classification

Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll

Low-Dimensional Density Ratio Estimation for Covariate Shift Correction

Petar Stojanov, Mingming Gong, Jaime Carbonell, Kun Zhang

Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity

Kohei Miyaguchi, Kenji Yamanishi

Structured Neural Topic Models for Reviews

Babak Esmaeili, Hongyi Huang, Byron Wallace, Jan-Willem van de Meent

Convergence of Gradient Descent on Separable Data

Mor Shpigel Nacson, Jason Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro

Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design

Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue

ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery

Raj Agrawal, Chandler Squires, Karren Yang, Karthikeyan Shanmugam, Caroline Uhler

Representation Learning on Graphs: A Reinforcement Learning Application

Sephora Madjiheurem, Laura Toni

Interpreting Black Box Predictions using Fisher Kernels

Rajiv Khanna, Been Kim, Joydeep Ghosh, Sanmi Koyejo

Efficient Bayes Risk Estimation for Cost-Sensitive Classification

Daniel Andrade, Yuzuru Okajima

Multitask Metric Learning: Theory and Algorithm

Boyu Wang, Hejia Zhang, Peng Liu, Zebang Shen, Joelle Pineau

Fixing Mini-batch Sequences with Hierarchical Robust Partitioning

Shengjie Wang, Wenruo Bai, Chandrashekhar Lavania, Jeff Bilmes

Robustness Guarantees for Density Clustering

Heinrich Jiang, Jennifer Jang, Ofir Nachum

Sample Efficient Graph-Based Optimization with Noisy Observations

Thanh Tan Nguyen, Ali Shameli, Yasin Abbasi-Yadkori, Anup Rao, Branislav Kveton

Recovery Guarantees For Quadratic Tensors With Sparse Observations

Hongyang Zhang, Vatsal Sharan, Moses Charikar, Yingyu Liang

Active Ranking with Subset-wise Preferences

Aadirupa Saha, Aditya Gopalan

Adversarial Learning of a Sampler Based on an Unnormalized Distribution

Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen

The LORACs Prior for VAEs: Letting the Trees Speak for the Data

Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson

Multiscale Gaussian Process Level Set Estimation

Shubhanshu Shekhar, Tara Javidi

Harmonizable mixture kernels with variational Fourier features

Zheyang Shen, Markus Heinonen, Samuel Kaski

Multi-Order Information for Working Set Selection of Sequential Minimal Optimization

Qimao Yang, Changrong Li, Jun Guo

Revisit Batch Normalization: New Understanding and Refinement via Composition Optimization

Xiangru Lian, Ji Liu

Reparameterizing Distributions on Lie Groups

Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré

Size of Interventional Markov Equivalence Classes in random DAG models

Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires, Caroline Uhler

Database Alignment with Gaussian Features

Osman E. Dai, Daniel Cullina, Negar Kiyavash

Domain-Size Aware Markov Logic Networks

Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla

Lifting high-dimensional non-linear models with Gaussian regressors

CHRISTOS THRAMPOULIDIS, Ankit Singh Rawat

Online Learning in Kernelized Markov Decision Processes

Sayak Ray Chowdhury, Aditya Gopalan

Accelerating Imitation Learning with Predictive Models

Ching-An Cheng, Xinyan Yan, Evangelos Theodorou, Byron Boots

Best of many worlds: Robust model selection for online supervised learning

Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter Bartlett

Online Algorithm for Unsupervised Sensor Selection

Arun Verma, Manjesh Hanawal, Csaba Szepesvári, Venkatesh Saligrama

A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes

Jialin Song, Yuxin Chen, Yisong Yue

Probabilistic Multilevel Clustering via Composite Transportation Distance

nhật Hồ, Viet Huynh, Dinh Phung, Michael Jordan

Sample-Efficient Imitation Learning via Generative Adversarial Nets

Lionel Blondé, Alexandros Kalousis

Structured Robust Submodular Maximization: Offline and Online Algorithms

Nima Anari, Nika Haghtalab, Seffi Naor, Sebastian Pokutta, Mohit Singh

Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport

Adarsh Subbaswamy, Peter Schulam, Suchi Saria

Model-Free Linear Quadratic Control via Reduction to Expert Prediction

Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvári

Adaptive Estimation for Approximate k-Nearest-Neighbor Computations

Daniel LeJeune, Reinhard Heckel, Richard Baraniuk

Adversarial Discrete Sequence Generation without Explicit NeuralNetworks as Discriminators

Zhongliang Li, Tian Xia, Xingyu Lou, Kaihe Xu, Shaojun Wang

A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects

Daniel Malinsky, Ilya Shpitser, Thomas Richardson

A recurrent Markov state-space generative model for sequences

Anand Ramachandran, Steve Lumetta, Eric Klee, Deming Chen

Deep Switch Networks for Generating Discrete Data and Language

Payam Delgosha, Naveen Goela

Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate

Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry

Optimizing over a Restricted Policy Class in MDPs

Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh, Nikos Vlassis

Improved Semi-Supervised Learning with Multiple Graphs

Krishnamurthy Viswanathan, Sushant Sachdeva, Andrew Tomkins, Sujith Ravi

Binary Space Partitioning Forest

Xuhui Fan, Bin Li, Scott SIsson

Minimum Volume Topic Modeling

Byoungwook Jang, Alfred Hero

A Fast Sampling Algorithm for Maximum Inner Product Search

QIN DING, Hsiang-Fu Yu, Cho-jui Hsieh

Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning

Tadashi Kozuno, Eiji Uchibe, Kenji Doya

Learning the Structure of a Nonstationary Vector Autoregression

Daniel Malinsky, Peter Spirtes

Sobolev Descent

Youssef Mroueh, Tom Sercu, Anant Raj

A maximum-mean-discrepancy goodness-of-fit test for censored data

Tamara Fernandez, Arthur Gretton

Imitation-Regularized Offline Learning

Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy

Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation

Mingming Sun, Ping Li

Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics

Difan Zou, Pan Xu, Quanquan Gu

Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective

Anirudh Vemula, Wen Sun, J. Bagnell

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems

Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter Bartlett

Adaptive Rao-Blackwellisation in Gibbs Sampling for Probabilistic Graphical Models

Craig Kelly, Somdeb Sarkhel, Deepak Venugopal

Decentralized Gradient Tracking for Continuous DR-Submodular Maximization

Jiahao Xie, Chao Zhang, Zebang Shen, Chao Mi, Hui Qian

Efficient Greedy Coordinate Descent for Composite Problems

Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi

Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference

Mike Wu, Noah Goodman, Stefano Ermon

Complexities in Projection-Free Stochastic Non-convex Minimization

Zebang Shen, Cong Fang, Peilin Zhao, Junzhou Huang, Hui Qian

On the Dynamics of Gradient Descent for Autoencoders

Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde

Deep Topic Models for Multi-label Learning

Rajat Panda, Ankit Pensia, Nikhil Mehta, Mingyuan Zhou, Piyush Rai

On Theory for BART

Veronika Ročková, Enakshi Saha

AutoML from Service Provider’s Perspective: Multi-device, Multi-tenant Model Selection with GP-EI

Chen Yu, Bojan Karlaš, Jie Zhong, Ce Zhang, Ji Liu

Exploring k out of Top ρ Fraction of Arms in Stochastic Bandits

Wenbo Ren, Jia Liu, Ness B. Shroff

Precision Matrix Estimation with Noisy and Missing Data

Roger Fan, Byoungwook Jang, Yuekai Sun, Shuheng Zhou

Region-Based Active Learning

Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang

A Unified Weight Learning Paradigm for Multi-view Learning

Lai Tian, Feiping Nie, Xuelong Li

Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era

Nicolas Durrande, Vincent ADAM, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman

Cost aware Inference for IoT Devices

Pengkai Zhu, Durmus Alp Emre Acar, Nan Feng, Prateek Jain, Venkatesh Saligrama

High Dimensional Inference in Partially Linear Models

Ying Zhu, Zhuqing Yu, Guang Cheng

Improving Quadrature for Constrained Integrands

Henry R. Chai, Roman Garnett

Variational Noise-Contrastive Estimation

Benjamin Rhodes, Michael U. Gutmann

Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization

Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan

Hierarchical Clustering for Euclidean Data

Moses Charikar, Vaggos Chatziafratis, Rad Niazadeh, Grigory Yaroslavtsev

Variance reduction properties of the reparameterization trick

Ming Xu, Matias Quiroz, Robert Kohn, Scott A. Sisson

Adaptive MCMC via Combining Local Samplers

Kiarash Shaloudegi, András György

Multi-Observation Regression

Rafael Frongillo, Nishant A. Mehta, Tom Morgan, Bo Waggoner

Interpolating between Optimal Transport and MMD using Sinkhorn Divergences

Jean Feydy, Thibault Sejourne, Francois-Xavier Vialard, Shun-ichi Amari, Alain Trouve

Correspondence Analysis Using Neural Networks

Hsiang Hsu, Salman Salamatian, Flavio P. Calmon

Efficient Bayesian Optimization for Target Vector Estimation

Anders Kirk Uhrenholt, Bjøern Sand Jensen

Credit Assignment Techniques in Stochastic Computation Graphs

Theophane Weber, Nicolas Heess, Lars Buesing, David Silver

Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables

Hsiang-Fu Yu, Cho-jui Hsieh, Inderjit S. Dhillon

Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference

Kelvin Hsu, Fabio Ramos

A Higher-Order Kolmogorov-Smirnov Test

Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani

Scalable High-Order Gaussian Process Regression

Shandian Zhe, Wei Xing, Robert M. Kirby

Reducing training time by efficient localized kernel regression

Nicole Muecke

Doubly Semi-Implicit Variational Inference

Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry Vetrov

Overcomplete Independent Component Analysis via SDP

Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis Bach, Alexandre d’Aspremont

A Topological Regularizer for Classifiers via Persistent Homology

Chao Chen, Xiuyan Ni, Qinxun Bai, Yusu Wang

Rotting bandits are no harder than stochastic ones

Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko

Estimation of Non-Normalized Mixture Models

Takeru Matsuda, Aapo Hyvärinen

Locally Private Mean Estimation: Z-test and Tight Confidence Intervals

Marco Gaboardi, Ryan Rogers, Or Sheffet

Estimating Network Structure from Incomplete Event Data

Benjamin Mark, Garvesh Raskutti, Rebecca Willett

Structured Disentangled Representations

Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N Siddharth