AISTATS 2017

167 papers

Scalable Greedy Feature Selection via Weak Submodularity

Rajiv Khanna, Ethan Elenberg, Alex Dimakis, Sahand Negahban, Joydeep Ghosh

Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain

Xiangru Huang, Ian En-Hsu Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar

Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models

Ankit Anand, Ritesh Noothigattu, Parag Singla, Mausam

Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions

Asish Ghoshal, Jean Honorio

Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness

Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition

Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit Dhillon

Automated Inference with Adaptive Batches

Soham De, Abhay Yadav, David Jacobs, Tom Goldstein

Convergence Rate of Stochastic k-means

Cheng Tang, Claire Monteleoni

Value-Aware Loss Function for Model-based Reinforcement Learning

Amir-massoud Farahmand, Andre Barreto, Daniel Nikovski

A Maximum Matching Algorithm for Basis Selection in Spectral Learning

Ariadna Quattoni, Xavier Carreras, Matthias Gallé

Sequential Multiple Hypothesis Testing with Type I Error Control

Alan Malek, Sumeet Katariya, Yinlam Chow, Mohammad Ghavamzadeh

Learning from Conditional Distributions via Dual Embeddings

Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song

Label Filters for Large Scale Multilabel Classification

Alexandru Niculescu-mizil, Ehsan Abbasnejad

Spectral Methods for Correlated Topic Models

Forough Arabshahi, Anima Anandkumar

Binary and Multi-Bit Coding for Stable Random Projections

Ping Li

Distributed Adaptive Sampling for Kernel Matrix Approximation

Daniele Calandriello, Alessandro Lazaric, Michal Valko

Distribution of Gaussian Process Arc Lengths

Justin Bewsher, Alessandra Tosi, Michael Osborne, Stephen Roberts

Efficient Online Multiclass Prediction on Graphs via Surrogate Losses

Alexander Rakhlin, Karthik Sridharan

Optimal Recovery of Tensor Slices

Vivek Farias, Andrew Li

Initialization and Coordinate Optimization for Multi-way Matching

Da Tang, Tony Jebara

Linking Micro Event History to Macro Prediction in Point Process Models

Yichen Wang, Xiaojing Ye, Haomin Zhou, Hongyuan Zha, Le Song

On the Interpretability of Conditional Probability Estimates in the Agnostic Setting

Yihan Gao, Aditya Parameswaran, Jian Peng

Information Projection and Approximate Inference for Structured Sparse Variables

Rajiv Khanna, Joydeep Ghosh, Rusell Poldrack, Oluwasanmi Koyejo

Active Positive Semidefinite Matrix Completion: Algorithms, Theory and Applications

Aniruddha Bhargava, Ravi Ganti, Rob Nowak

Inference Compilation and Universal Probabilistic Programming

Tuan Anh Le, Atilim Gunes Baydin, Frank Wood

Spatial Decompositions for Large Scale SVMs

Philipp Thomann, Ingrid Blaschzyk, Mona Meister, Ingo Steinwart

Lipschitz Density-Ratios, Structured Data, and Data-driven Tuning

Samory Kpotufe

Discovering and Exploiting Additive Structure for Bayesian Optimization

Jacob Gardner, Chuan Guo, Kilian Weinberger, Roman Garnett, Roger Grosse

Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields

Youngsuk Park, David Hallac, Stephen Boyd, Jure Leskovec

Signal-based Bayesian Seismic Monitoring

David Moore, Stuart Russell

Learning Structured Weight Uncertainty in Bayesian Neural Networks

Shengyang Sun, Changyou Chen, Lawrence Carin

Communication-Efficient Learning of Deep Networks from Decentralized Data

Brendan Mcmahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Aguera Y Arcas

Prediction Performance After Learning in Gaussian Process Regression

Johan Wagberg, Dave Zachariah, Thomas Schön, Petre Stoica

Fast Classification with Binary Prototypes

Kai Zhong, Ruiqi Guo, Sanjiv Kumar, Bowei Yan, David Simcha

Thompson Sampling for Linear-Quadratic Control Problems

Marc Abeille, Alessandro Lazaric

Relativistic Monte Carlo

Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian Vollmer

Local Group Invariant Representations via Orbit Embeddings

Anant Raj, Abhishek Kumar, Youssef Mroueh, Tom Fletcher, Bernhard Schoelkopf

Diverse Neural Network Learns True Target Functions

Bo Xie, Yingyu Liang, Le Song

Compressed Least Squares Regression revisited

Martin Slawski

Modal-set estimation with an application to clustering

Heinrich Jiang, Samory Kpotufe

Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage

Alp Yurtsever, Madeleine Udell, Joel Tropp, Volkan Cevher

Communication-efficient Distributed Sparse Linear Discriminant Analysis

Lu Tian, Quanquan Gu

A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models

Beilun Wang, Ji Gao, Yanjun Qi

Finite-sum Composition Optimization via Variance Reduced Gradient Descent

Xiangru Lian, Mengdi Wang, Ji Liu

Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data

Jialei Wang, Jason Lee, Mehrdad Mahdavi, Mladen Kolar, Nati Srebro

Belief Propagation in Conditional RBMs for Structured Prediction

Wei Ping, Alex Ihler

Tracking Objects with Higher Order Interactions via Delayed Column Generation

Shaofei Wang, Steffen Wolf, Charless Fowlkes, Julian Yarkony

Identifying Groups of Strongly Correlated Variables through Smoothed Ordered Weighted L_1-norms

Raman Sankaran, Francis Bach, Chiranjib Bhattacharya

Optimistic Planning for the Stochastic Knapsack Problem

Ciara Pike-Burke, Steffen Grünewälder

Learning with Feature Feedback: from Theory to Practice

Stefanos Poulis, Sanjoy Dasgupta

Generalization Error of Invariant Classifiers

Jure Sokolic, Raja Giryes, Guillermo Sapiro, Miguel Rodrigues

Fast rates with high probability in exp-concave statistical learning

Nishant Mehta

Sequential Graph Matching with Sequential Monte Carlo

Seong-hwan Jun, Samuel W.K. Wong, James Zidek, Alexandre Bouchard-côté

Linear Convergence of Stochastic Frank Wolfe Variants

Donald Goldfarb, Garud Iyengar, Chaoxu Zhou

Scalable Variational Inference for Super Resolution Microscopy

Ruoxi Sun, Evan Archer, Liam Paninski

A Lower Bound on the Partition Function of Attractive Graphical Models in the Continuous Case

Nicholas Ruozzi

Learning Optimal Interventions

Jonas Mueller, David Reshef, George Du, Tommi Jaakkola

Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification

Aleksandar Botev, Bowen Zheng, David Barber

Structured adaptive and random spinners for fast machine learning computations

Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Francois Fagan, Cedric Gouy-Pailler

Anomaly Detection in Extreme Regions via Empirical MV-sets on the Sphere

Albert Thomas, Stéphan Clémençon, Alexandre Gramfort, Anne Sabourin

A Learning Theory of Ranking Aggregation

Anna Korba, Stéphan Clémençon, Eric Sibony

A New Class of Private Chi-Square Hypothesis Tests

Ryan Rogers, Daniel Kifer

A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation

Lingxiao Wang, Xiao Zhang, Quanquan Gu

Frequency Domain Predictive Modelling with Aggregated Data

Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo

Fairness Constraints: Mechanisms for Fair Classification

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, Krishna P. Gummadi

Black-box Importance Sampling

Qiang Liu, Jason Lee

Improved Strongly Adaptive Online Learning using Coin Betting

Kwang-Sung Jun, Francesco Orabona, Stephen Wright, Rebecca Willett

Minimax-optimal semi-supervised regression on unknown manifolds

Amit Moscovich, Ariel Jaffe, Nadler Boaz

Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent

Pan Xu, Tingting Zhang, Quanquan Gu

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems

Scott Linderman, Matthew Johnson, Andrew Miller, Ryan Adams, David Blei

On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior

Juho Piironen, Aki Vehtari

DP-EM: Differentially Private Expectation Maximization

Mijung Park, James Foulds, Kamalika Choudhary, Max Welling

Hit-and-Run for Sampling and Planning in Non-Convex Spaces

Yasin Abbasi-Yadkori, Peter Bartlett, Victor Gabillon, Alan Malek

Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models

Mohammad Khan, Wu Lin

Faster Coordinate Descent via Adaptive Importance Sampling

Dmytro Perekrestenko, Volkan Cevher, Martin Jaggi

A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe

Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi

Comparison-Based Nearest Neighbor Search

Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike Von Luxburg

CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC

Tianfan Fu, Zhihua Zhang

Scalable Learning of Non-Decomposable Objectives

Elad Eban, Mariano Schain, Alan Mackey, Ariel Gordon, Ryan Rifkin

Hierarchically-partitioned Gaussian Process Approximation

Byung-Jun Lee, Jongmin Lee, Kee-eung Kim

A Stochastic Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization

Songtao Lu, Mingyi Hong, Zhengdao Wang

Unsupervised Sequential Sensor Acquisition

Manjesh Hanawal, Csaba Szepesvári, Venkatesh Saligrama

Attributing Hacks

Ziqi Liu, Alex Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng

Regression Uncertainty on the Grassmannian

Yi Hong, Xiao Yang, Roland Kwitt, Martin Styner, Marc Niethammer

Markov Chain Truncation for Doubly-Intractable Inference

Colin Wei, Iain Murray

Information-theoretic limits of Bayesian network structure learning

Asish Ghoshal, Jean Honorio

Robust and Efficient Computation of Eigenvectors in a Generalized Spectral Method for Constrained Clustering

Chengming Jiang, Huiqing Xie, Zhaojun Bai

Rank Aggregation and Prediction with Item Features

Kai-Yang Chiang, Cho-jui Hsieh, Inderjit Dhillon

Dynamic Collaborative Filtering With Compound Poisson Factorization

Ghassen Jerfel, Mehmet Basbug, Barbara Engelhardt

The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits

Tor Lattimore, Csaba Szepesvári

Adaptive ADMM with Spectral Penalty Parameter Selection

Zheng Xu, Mário Figueiredo, Tom Goldstein

Trading off Rewards and Errors in Multi-Armed Bandits

Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yun-En Liu

Random projection design for scalable implicit smoothing of randomly observed stochastic processes

Francois Belletti, Evan Sparks, Alexandre Bayen, Joseph Gonzalez

Horde of Bandits using Gaussian Markov Random Fields

Sharan Vaswani, Mark Schmidt, Laks Lakshmanan

Sparse Randomized Partition Trees for Nearest Neighbor Search

Kaushik Sinha, Omid Keivani

Learning Nonparametric Forest Graphical Models with Prior Information

Yuancheng Zhu, Zhe Liu, Siqi Sun

Data Driven Resource Allocation for Distributed Learning

Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan

Minimax Approach to Variable Fidelity Data Interpolation

Alexey Zaytsev, Evgeny Burnaev

High-dimensional Time Series Clustering via Cross-Predictability

Dezhi Hong, Quanquan Gu, Kamin Whitehouse

Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes

Feras Saad, Vikash Mansinghka