AISTATS 2018

216 papers

Dimensionality Reduced ℓ^0-Sparse Subspace Clustering

Yingzhen Yang

Bootstrapping EM via Power EM and Convergence in the Naive Bayes Model

Costis Daskalakis, Christos Tzamos, Manolis Zampetakis

A Unified Dynamic Approach to Sparse Model Selection

Chendi Huang, Yuan Yao

Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments

Yanning Shen, Tianyi Chen, Georgios Giannakis

Derivative Free Optimization Via Repeated Classification

Tatsunori Hashimoto, Steve Yadlowsky, John Duchi

Probability–Revealing Samples

Krzysztof Onak, Xiaorui Sun

HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step

Yuting Ye, Lihua Lei, Cheng Ju

Gradient Diversity: a Key Ingredient for Scalable Distributed Learning

Dong Yin, Ashwin Pananjady, Max Lam, Dimitris Papailiopoulos, Kannan Ramchandran

Labeled Graph Clustering via Projected Gradient Descent

Shiau Hong Lim, Gregory Calvez

Learning Hidden Quantum Markov Models

Siddarth Srinivasan, Geoff Gordon, Byron Boots

Near-Optimal Machine Teaching via Explanatory Teaching Sets

Yuxin Chen, Oisin Mac Aodha, Shihan Su, Pietro Perona, Yisong Yue

Proximity Variational Inference

Jaan Altosaar, Rajesh Ranganath, David Blei

Nonparametric Bayesian sparse graph linear dynamical systems

Rahi Kalantari, Joydeep Ghosh, Mingyuan Zhou

Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences

He Zhao, Piyush Rai, Lan Du, Wray Buntine

Spectral Algorithms for Computing Fair Support Vector Machines

Mahbod Olfat, Anil Aswani

The emergence of spectral universality in deep networks

Jeffrey Pennington, Samuel Schoenholz, Surya Ganguli

Matrix-normal models for fMRI analysis

Michael Shvartsman, Narayanan Sundaram, Mikio Aoi, Adam Charles, Theodore Willke

Efficient Bayesian Methods for Counting Processes in Partially Observable Environments

Ferdian Jovan, Jeremy Wyatt, Nick Hawes

Online Continuous Submodular Maximization

Lin Chen, Hamed Hassani, Amin Karbasi

Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap

Aryan Mokhtari, Hamed Hassani, Amin Karbasi

Scalable Hash-Based Estimation of Divergence Measures

Morteza Noshad, Alfred Hero

On denoising modulo 1 samples of a function

Mihai Cucuringu, Hemant Tyagi

The Binary Space Partitioning-Tree Process

Xuhui Fan, Bin Li, Scott SIsson

Comparison Based Learning from Weak Oracles

Ehsan Kazemi, Lin Chen, Sanjoy Dasgupta, Amin Karbasi

Adaptive Sampling for Coarse Ranking

Sumeet Katariya, Lalit Jain, Nandana Sengupta, James Evans, Robert Nowak

The Power Mean Laplacian for Multilayer Graph Clustering

Pedro Mercado, Antoine Gautier, Francesco Tudisco, Matthias Hein

Parallel and Distributed MCMC via Shepherding Distributions

Arkabandhu Chowdhury, Christopher Jermaine

Inference in Sparse Graphs with Pairwise Measurements and Side Information

Dylan Foster, Karthik Sridharan, Daniel Reichman

Convergence of Value Aggregation for Imitation Learning

Ching-An Cheng, Byron Boots

Asynchronous Doubly Stochastic Group Regularized Learning

Bin Gu, Zhouyuan Huo, Heng Huang

Graphical Models for Non-Negative Data Using Generalized Score Matching

Shiqing Yu, Mathias Drton, Ali Shojaie

Structured Optimal Transport

David Alvarez-Melis, Tommi Jaakkola, Stefanie Jegelka

Combinatorial Semi-Bandits with Knapsacks

Karthik Abinav Sankararaman, Aleksandrs Slivkins

Robust Locally-Linear Controllable Embedding

Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Bui, Ali Ghodsi

Reducing Crowdsourcing to Graphon Estimation, Statistically

Devavrat Shah, Christina Lee

Competing with Automata-based Expert Sequences

Mehryar Mohri, Scott Yang

Practical Bayesian optimization in the presence of outliers

Ruben Martinez-Cantin, Kevin Tee, Michael McCourt

Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy

Bai Jiang

Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control

Sanket Kamthe, Marc Deisenroth

Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

Beilun Wang, Arshdeep Sekhon, Yanjun Qi

Turing: A Language for Flexible Probabilistic Inference

Hong Ge, Kai Xu, Zoubin Ghahramani

Multi-objective Contextual Bandit Problem with Similarity Information

Eralp Turgay, Doruk Oner, Cem Tekin

Weighted Tensor Decomposition for Learning Latent Variables with Partial Data

Omer Gottesman, Weiwei Pan, Finale Doshi-velez

Solving lp-norm regularization with tensor kernels

Saverio Salzo, Lorenzo Rosasco, Johan Suykens

Learning with Complex Loss Functions and Constraints

Harikrishna Narasimhan

Efficient Weight Learning in High-Dimensional Untied MLNs

Khan Mohammad Al Farabi, Somdeb Sarkhel, Deepak Venugopal

Achieving the time of 1-NN, but the accuracy of k-NN

Lirong Xue, Samory Kpotufe

Reparameterizing the Birkhoff Polytope for Variational Permutation Inference

Scott Linderman, Gonzalo Mena, Hal Cooper, Liam Paninski, John Cunningham

Learning Generative Models with Sinkhorn Divergences

Aude Genevay, Gabriel Peyré, Marco Cuturi

Online Regression with Partial Information: Generalization and Linear Projection

Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga

Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems

Jason Ge, Zhaoran Wang, Mengdi Wang, Han Liu

On Statistical Optimality of Variational Bayes

Debdeep Pati, Anirban Bhattacharya, Yun Yang

Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth

Jussi Viinikka, Ralf Eggeling, Mikko Koivisto

Generalized Binary Search For Split-Neighborly Problems

Stephen Mussmann, Percy Liang

Combinatorial Penalties: Which structures are preserved by convex relaxations?

Marwa El Halabi, Francis Bach, Volkan Cevher

Metrics for Deep Generative Models

Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer

On the Statistical Efficiency of Compositional Nonparametric Prediction

Yixi Xu, Jean Honorio, Xiao Wang

Factorized Recurrent Neural Architectures for Longer Range Dependence

Francois Belletti, Alex Beutel, Sagar Jain, Ed Chi

Plug-in Estimators for Conditional Expectations and Probabilities

Steffen Grünewälder

Stochastic algorithms for entropy-regularized optimal transport problems

Brahim Khalil Abid, Robert Gower

Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization

Seung-Jean Kim, Johan Lim, Joong-Ho Won

Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity

Asish Ghoshal, Jean Honorio

Convergence diagnostics for stochastic gradient descent with constant learning rate

Jerry Chee, Panos Toulis

Learning linear structural equation models in polynomial time and sample complexity

Asish Ghoshal, Jean Honorio

Frank-Wolfe Splitting via Augmented Lagrangian Method

Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien

Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method

Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro

Bayesian Structure Learning for Dynamic Brain Connectivity

Michael Andersen, Ole Winther, Lars Kai Hansen, Russell Poldrack, Oluwasanmi Koyejo

Scalable Generalized Dynamic Topic Models

Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt

Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation

Mohammadreza Soltani, Chinmay Hegde

Product Kernel Interpolation for Scalable Gaussian Processes

Jacob Gardner, Geoff Pleiss, Ruihan Wu, Kilian Weinberger, Andrew Wilson

Fast generalization error bound of deep learning from a kernel perspective

Taiji Suzuki

Robust Vertex Enumeration for Convex Hulls in High Dimensions

Pranjal Awasthi, Bahman Kalantari, Yikai Zhang

Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation

Penporn Koanantakool, Alnur Ali, Ariful Azad, Aydin Buluc, Dmitriy Morozov

Teacher Improves Learning by Selecting a Training Subset

Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu

Stochastic Zeroth-order Optimization in High Dimensions

Yining Wang, Simon Du, Sivaraman Balakrishnan, Aarti Singh

Linear Stochastic Approximation: How Far Does Constant Step-Size and Iterate Averaging Go?

Chandrashekar Lakshminarayanan, Csaba Szepesvári

Kernel Conditional Exponential Family

Michael Arbel, Arthur Gretton

Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning

Shashank Singh, Barnabás Póczos, Jian Ma

Cheap Checking for Cloud Computing: Statistical Analysis via Annotated Data Streams

Chris Hickey, Graham Cormode

Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models

Ardavan Saeedi, Matthew Hoffman, Stephen DiVerdi, Asma Ghandeharioun, Matthew Johnson

Sketching for Kronecker Product Regression and P-splines

Huaian Diao, Zhao Song, Wen Sun, David Woodruff

Nested CRP with Hawkes-Gaussian Processes

Xi Tan, Vinayak Rao, Jennifer Neville

Robustness of classifiers to uniform ℓ_p and Gaussian noise

Jean-Yves Franceschi, Alhussein Fawzi, Omar Fawzi

Sparse Linear Isotonic Models

Sheng Chen, Arindam Banerjee

Why Adaptively Collected Data Have Negative Bias and How to Correct for It

Xinkun Nie, Xiaoying Tian, Jonathan Taylor, James Zou

Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations

Alan Lazarus, Dirk Husmeier, Theodore Papamarkou

Policy Evaluation and Optimization with Continuous Treatments

Nathan Kallus, Angela Zhou

A Generic Approach for Escaping Saddle points

Sashank Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis Bach

Structured Factored Inference for Probabilistic Programming

Avi Pfeffer, Brian Ruttenberg, William Kretschmer, Alison OConnor

VAE with a VampPrior

Jakub Tomczak, Max Welling

Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems

Sai Praneeth Reddy Karimireddy, Sebastian Stich, Martin Jaggi

Differentially Private Regression with Gaussian Processes

Michael T. Smith, Mauricio A. Álvarez, Max Zwiessele, Neil D. Lawrence

Provable Estimation of the Number of Blocks in Block Models

Bowei Yan, Purnamrita Sarkar, Xiuyuan Cheng

Submodularity on Hypergraphs: From Sets to Sequences

Marko Mitrovic, Moran Feldman, Andreas Krause, Amin Karbasi

Bayesian Approaches to Distribution Regression

Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth Flaxman

Optimality of Approximate Inference Algorithms on Stable Instances

Hunter Lang, David Sontag, Aravindan Vijayaraghavan

Towards Memory-Friendly Deterministic Incremental Gradient Method

Jiahao Xie, Hui Qian, Zebang Shen, Chao Zhang

Learning Structural Weight Uncertainty for Sequential Decision-Making

Ruiyi Zhang, Chunyuan Li, Changyou Chen, Lawrence Carin

Crowdclustering with Partition Labels

Junxiang Chen, Yale Chang, Peter Castaldi, Michael Cho, Brian Hobbs

Factor Analysis on a Graph

Masayuki Karasuyama, Hiroshi Mamitsuka