AISTATS 2017

167 papers

Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers

Meelis Kull, Telmo Silva Filho, Peter Flach

Tensor Decompositions via Two-Mode Higher-Order SVD (HOSVD)

Miaoyan Wang, Yun Song

Online Learning and Blackwell Approachability with Partial Monitoring: Optimal Convergence Rates

Joon Kwon, Vianney Perchet

Gradient Boosting on Stochastic Data Streams

Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, Andrew Bagnell

Local Perturb-and-MAP for Structured Prediction

Gedas Bertasius, Qiang Liu, Lorenzo Torresani, Jianbo Shi

Exploration-Exploitation in MDPs with Options

Ronan Fruit, Alessandro Lazaric

Co-Occurring Directions Sketching for Approximate Matrix Multiply

Youssef Mroueh, Etienne Marcheret, Vaibahava Goel

Bayesian Hybrid Matrix Factorisation for Data Integration

Thomas Brouwer, Pietro Lió

Fast column generation for atomic norm regularization

Marina Vinyes, Guillaume Obozinski

Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds

Mina Ashizawa, Hiroaki Sasaki, Tomoya Sakai, Masashi Sugiyama

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter

Contextual Bandits with Latent Confounders: An NMF Approach

Rajat Sen, Karthikeyan Shanmugam, Murat Kocaoglu, Alex Dimakis, Sanjay Shakkottai

Decentralized Collaborative Learning of Personalized Models over Networks

Paul Vanhaesebrouck, Aurélien Bellet, Marc Tommasi

Asymptotically exact inference in differentiable generative models

Matthew Graham, Amos Storkey

Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms

Christian Naesseth, Francisco Ruiz, Scott Linderman, David Blei

Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot

Prateek Jain, Chi Jin, Sham Kakade, Praneeth Netrapalli

Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines

Atsushi Nitanda, Taiji Suzuki

Nonlinear ICA of Temporally Dependent Stationary Sources

Aapo Hyvärinen, Hiroshi Morioka

Efficient Rank Aggregation via Lehmer Codes

Pan Li, Arya Mazumdar, Olgica Milenkovic

Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models

Sejun Park, Yunhun Jang, Andreas Galanis, Jinwoo Shin, Daniel Stefankovic

ConvNets with Smooth Adaptive Activation Functions for Regression

Le Hou, Dimitris Samaras, Tahsin Kurc, Yi Gao, Joel Saltz

Combinatorial Topic Models using Small-Variance Asymptotics

Ke Jiang, Suvrit Sra, Brian Kulis

Online Optimization of Smoothed Piecewise Constant Functions

Vincent Cohen-Addad, Varun Kanade

On the Troll-Trust Model for Edge Sign Prediction in Social Networks

Géraud Le Falher, Nicolò Cesa-bianchi, Claudio Gentile, Fabio Vitale

Stochastic Rank-1 Bandits

Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen

Quantifying the accuracy of approximate diffusions and Markov chains

Jonathan Huggins, James Zou

A Framework for Optimal Matching for Causal Inference

Nathan Kallus

Frank-Wolfe Algorithms for Saddle Point Problems

Gauthier Gidel, Tony Jebara, Simon Lacoste-Julien

Gray-box Inference for Structured Gaussian Process Models

Pietro Galliani, Amir Dezfouli, Edwin Bonilla, Novi Quadrianto

Random Consensus Robust PCA

Daniel Pimentel-Alarcon, Robert Nowak

Encrypted Accelerated Least Squares Regression

Pedro Esperança, Louis Aslett, Chris Holmes

Localized Lasso for High-Dimensional Regression

Makoto Yamada, Takeuchi Koh, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski

Scaling Submodular Maximization via Pruned Submodularity Graphs

Tianyi Zhou, Hua Ouyang, Jeff Bilmes, Yi Chang, Carlos Guestrin

Regret Bounds for Transfer Learning in Bayesian Optimisation

Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh

Performance Bounds for Graphical Record Linkage

Rebecca C. Steorts, Mattew Barnes, Willie Neiswanger

Removing Phase Transitions from Gibbs Measures

Ian Fellows, Mark Handcock

Generalized Pseudolikelihood Methods for Inverse Covariance Estimation

Alnur Ali, Kshitij Khare, Sang-Yun Oh, Bala Rajaratnam

Poisson intensity estimation with reproducing kernels

Seth Flaxman, Yee Whye Teh, Dino Sejdinovic

Regret Bounds for Lifelong Learning

Pierre Alquier, The Tien Mai, Massimiliano Pontil

Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation

Sohail Bahmani, Justin Romberg

Distance Covariance Analysis

Benjamin Cowley, Joao Semedo, Amin Zandvakili, Matthew Smith, Adam Kohn

Learning Nash Equilibrium for General-Sum Markov Games from Batch Data

Julien Pérolat, Florian Strub, Bilal Piot, Olivier Pietquin

Near-optimal Bayesian Active Learning with Correlated and Noisy Tests

Yuxin Chen, Hamed Hassani, Andreas Krause

Learning Theory for Conditional Risk Minimization

Alexander Zimin, Christoph Lampert

Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios

Hiroaki Sasaki, Takafumi Kanamori, Masashi Sugiyama

Minimax Density Estimation for Growing Dimension

Daniel McDonald

A Sub-Quadratic Exact Medoid Algorithm

James Newling, Francois Fleuret

Linear Thompson Sampling Revisited

Marc Abeille, Alessandro Lazaric

Learning Cost-Effective and Interpretable Treatment Regimes

Himabindu Lakkaraju, Cynthia Rudin

Learning Time Series Detection Models from Temporally Imprecise Labels

Roy Adams, Ben Marlin

Less than a Single Pass: Stochastically Controlled Stochastic Gradient

Lihua Lei, Michael Jordan

Annular Augmentation Sampling

Francois Fagan, Jalaj Bhandari, John Cunningham

Consistent and Efficient Nonparametric Different-Feature Selection

Satoshi Hara, Takayuki Katsuki, Hiroki Yanagisawa, Takafumi Ono, Ryo Okamoto

Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis

Andrew Stevens, Yunchen Pu, Yannan Sun, Gregory Spell, Lawrence Carin

Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains

Andrew An Bian, Baharan Mirzasoleiman, Joachim Buhmann, Andreas Krause

Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection

Lijie Chen, Jian Li, Mingda Qiao

An Information-Theoretic Route from Generalization in Expectation to Generalization in Probability

Ibrahim Alabdulmohsin

On the Learnability of Fully-Connected Neural Networks

Yuchen Zhang, Jason Lee, Martin Wainwright, Michael I. Jordan

Sparse Accelerated Exponential Weights

Pierre Gaillard, Olivier Wintenberger

Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach

Dohyung Park, Anastasios Kyrillidis, Constantine Carmanis, Sujay Sanghavi

Lower Bounds on Active Learning for Graphical Model Selection

Jonathan Scarlett, Volkan Cevher

ASAGA: Asynchronous Parallel SAGA

Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien

Online Nonnegative Matrix Factorization with General Divergences

Renbo Zhao, Vincent Tan, Huan Xu

Clustering from Multiple Uncertain Experts

Yale Chang, Junxiang Chen, Michael Cho, Peter Castaldi, Ed Silverman

Large-Scale Data-Dependent Kernel Approximation

Catalin Ionescu, Alin Popa, Cristian Sminchisescu

Conditions beyond treewidth for tightness of higher-order LP relaxations

Mark Rowland, Aldo Pacchiano, Adrian Weller

Minimax Gaussian Classification & Clustering

Tianyang Li, Xinyang Yi, Constantine Carmanis, Pradeep Ravikumar