AISTATS 2018

216 papers

Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling

Hongyi Ding, Mohammad Khan, Issei Sato, Masashi Sugiyama

A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery

Xiao Zhang, Lingxiao Wang, Quanquan Gu

Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms

Pan Xu, Tianhao Wang, Quanquan Gu

A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop

Yichen Wang, Evangelos Theodorou, Apurv Verma, Le Song

Semi-Supervised Prediction-Constrained Topic Models

Michael Hughes, Gabriel Hope, Leah Weiner, Thomas McCoy, Roy Perlis

Approximate Ranking from Pairwise Comparisons

Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin Wainwright

Learning to Round for Discrete Labeling Problems

Pritish Mohapatra, Jawahar C.V., M Pawan Kumar

Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

Lawrence Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas Schön

Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization

Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng

Statistical Sparse Online Regression: A Diffusion Approximation Perspective

Jianqing Fan, Wenyan Gong, Chris Junchi Li, Qiang Sun

Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models

Atsushi Nitanda, Taiji Suzuki

Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression

Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

SDCA-Powered Inexact Dual Augmented Lagrangian Method for Fast CRF Learning

Xu Hu, Guillaume Obozinski

Statistically Efficient Estimation for Non-Smooth Probability Densities

Masaaki Imaizumi, Takanori Maehara, Yuichi Yoshida

Variational Sequential Monte Carlo

Christian Naesseth, Scott Linderman, Rajesh Ranganath, David Blei

Convex Optimization over Intersection of Simple Sets: improved Convergence Rate Guarantees via an Exact Penalty Approach

Achintya Kundu, Francis Bach, Chiranjib Bhattacharya

Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures

Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos

Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information

Jakob Runge

Random Subspace with Trees for Feature Selection Under Memory Constraints

Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts

Actor-Critic Fictitious Play in Simultaneous Move Multistage Games

Julien Pérolat, Bilal Piot, Olivier Pietquin

Tree-based Bayesian Mixture Model for Competing Risks

Alexis Bellot, Mihaela Schaar

Cause-Effect Inference by Comparing Regression Errors

Patrick Bloebaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schoelkopf

Robust Maximization of Non-Submodular Objectives

Ilija Bogunovic, Junyao Zhao, Volkan Cevher

Smooth and Sparse Optimal Transport

Mathieu Blondel, Vivien Seguy, Antoine Rolet

Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient Algorithms

I Chien, Chung-Yi Lin, I-Hsiang Wang

Human Interaction with Recommendation Systems

Sven Schmit, Carlos Riquelme

Contextual Bandits with Stochastic Experts

Rajat Sen, Karthikeyan Shanmugam, Sanjay Shakkottai

A fully adaptive algorithm for pure exploration in linear bandits

Liyuan Xu, Junya Honda, Masashi Sugiyama

Best arm identification in multi-armed bandits with delayed feedback

Aditya Grover, Todor Markov, Peter Attia, Norman Jin, Nicolas Perkins

Variational Rejection Sampling

Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon

Variational Inference based on Robust Divergences

Futoshi Futami, Issei Sato, Masashi Sugiyama

Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD

Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, Priya Nagpurkar

Random Warping Series: A Random Features Method for Time-Series Embedding

Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei

One-shot Coresets: The Case of k-Clustering

Olivier Bachem, Mario Lucic, Silvio Lattanzi

Direct Learning to Rank And Rerank

Cynthia Rudin, Yining Wang

Stochastic Three-Composite Convex Minimization with a Linear Operator

Renbo Zhao, Volkan Cevher

Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series

Feras Saad, Vikash Mansinghka

Batched Large-scale Bayesian Optimization in High-dimensional Spaces

Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka

Batch-Expansion Training: An Efficient Optimization Framework

Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer

Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition

Pavel Izmailov, Alexander Novikov, Dmitry Kropotov

Subsampling for Ridge Regression via Regularized Volume Sampling

Michal Derezinski, Manfred Warmuth

Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods

Robert Gower, Nicolas Le Roux, Francis Bach

Variational inference for the multi-armed contextual bandit

Inigo Urteaga, Chris Wiggins

Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models

Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman

Nonlinear Weighted Finite Automata

Tianyu Li, Guillaume Rabusseau, Doina Precup

Few-shot Generative Modelling with Generative Matching Networks

Sergey Bartunov, Dmitry Vetrov

Symmetric Variational Autoencoder and Connections to Adversarial Learning

Liqun Chen, Shuyang Dai, Yunchen Pu, Erjin Zhou, Chunyuan Li

Efficient and principled score estimation with Nyström kernel exponential families

Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton

Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training

Mathieu Sinn, Ambrish Rawat

Nonparametric Preference Completion

Julian Katz-Samuels, Clayton Scott

Benefits from Superposed Hawkes Processes

Hongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin

Catalyst for Gradient-based Nonconvex Optimization

Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaïd Harchaoui

An Optimization Approach to Learning Falling Rule Lists

Chaofan Chen, Cynthia Rudin

Transfer Learning on fMRI Datasets

Hejia Zhang, Po-Hsuan Chen, Peter Ramadge

Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams

Shinsaku Sakaue, Masakazu Ishihata, Shin-ichi Minato

Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes

Hyunjik Kim, Yee Whye Teh

A Provable Algorithm for Learning Interpretable Scoring Systems

Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker

Group Invariance Principles for Causal Generative Models

Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing

Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods

Stéphan Clémençon, François Portier

Exploiting Strategy-Space Diversity for Batch Bayesian Optimization

Sunil Gupta, Alistair Shilton, Santu Rana, Svetha Venkatesh

Sum-Product-Quotient Networks

Or Sharir, Amnon Shashua

Nearly second-order optimality of online joint detection and estimation via one-sample update schemes

Yang Cao, Liyan Xie, Yao Xie, Huan Xu

Regional Multi-Armed Bandits

Zhiyang Wang, Ruida Zhou, Cong Shen

Medoids in Almost-Linear Time via Multi-Armed Bandits

Vivek Bagaria, Govinda Kamath, Vasilis Ntranos, Martin Zhang, David Tse

A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization

Emanuel Laude, Tao Wu, Daniel Cremers

Tensor Regression Meets Gaussian Processes

Rose Yu, Guangyu Li, Yan Liu

Personalized and Private Peer-to-Peer Machine Learning

Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, Marc Tommasi

Boosting Variational Inference: an Optimization Perspective

Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch

Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables

Masaaki Takada, Taiji Suzuki, Hironori Fujisawa

A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer

Tianbao Yang, Zhe Li, Lijun Zhang

Dropout as a Low-Rank Regularizer for Matrix Factorization

Jacopo Cavazza, Pietro Morerio, Benjamin Haeffele, Connor Lane, Vittorio Murino

Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data

William Herlands, Edward McFowland, Andrew Wilson, Daniel Neill

Multi-view Metric Learning in Vector-valued Kernel Spaces

Riikka Huusari, Hachem Kadri, Cécile Capponi

FLAG n’ FLARE: Fast Linearly-Coupled Adaptive Gradient Methods

Xiang Cheng, Fred Roosta, Stefan Palombo, Peter Bartlett, Michael Mahoney

Matrix completability analysis via graph k-connectivity

Dehua Cheng, Natali Ruchansky, Yan Liu

Stochastic Multi-armed Bandits in Constant Space

David Liau, Zhao Song, Eric Price, Ger Yang

Optimal Cooperative Inference

Scott Cheng-Hsin Yang, Yue Yu, Arash Givchi, Pei Wang, Wai Keen Vong

Learning Priors for Invariance

Eric Nalisnick, Padhraic Smyth

Topic Compositional Neural Language Model

Wenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang

Discriminative Learning of Prediction Intervals

Nir Rosenfeld, Yishay Mansour, Elad Yom-Tov

Semi-Supervised Learning with Competitive Infection Models

Nir Rosenfeld, Amir Globerson

Optimal Submodular Extensions for Marginal Estimation

Pankaj Pansari, Chris Russell, M Pawan Kumar

Factorial HMMs with Collapsed Gibbs Sampling for Optimizing Long-term HIV Therapy

Amit Gruber, Chen Yanover, Tal El-Hay, Anders Sönnerborg, Vanni Borghi

Robust Active Label Correction

Jan Kremer, Fei Sha, Christian Igel

High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups

Paul Rolland, Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher

Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications

Sijia Liu, Jie Chen, Pin-Yu Chen, Alfred Hero

Online Boosting Algorithms for Multi-label Ranking

Young Hun Jung, Ambuj Tewari

Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis

Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra

Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding

Kaiqing Zhang, Zhuoran Yang, Zhaoran Wang

Learning Determinantal Point Processes in Sublinear Time

Christophe Dupuy, Francis Bach

Online Learning with Non-Convex Losses and Non-Stationary Regret

Xiand Gao, XIAOBO LI, Shuzhong Zhang

AdaGeo: Adaptive Geometric Learning for Optimization and Sampling

Gabriele Abbati, Alessandra Tosi, Michael Osborne, Seth Flaxman

Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis

Luca Ambrogioni, Eric Maris

Outlier Detection and Robust Estimation in Nonparametric Regression

Dehan Kong, Howard Bondell, Weining Shen

Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction

Jinshan Zeng, Ke Ma, Yuan Yao

IHT dies hard: Provable accelerated Iterative Hard Thresholding

Rajiv Khanna, Anastasios Kyrillidis

Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond

Heng Guo, Kaan Kara, Ce Zhang

On Truly Block Eigensolvers via Riemannian Optimization

Zhiqiang Xu, Xin Gao

On how complexity affects the stability of a predictor

Joel Ratsaby

Post Selection Inference with Kernels

Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi