AISTATS 2015

126 papers

The Log-Shift Penalty for Adaptive Estimation of Multiple Gaussian Graphical Models

Yuancheng Zhu, Rina Foygel Barber

Power-Law Graph Cuts

Xiangyang Zhou, Jiaxin Zhang, Brian Kulis

Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction

Mingyuan Zhou

Scalable Nonparametric Multiway Data Analysis

Shandian Zhe, Zenglin Xu, Xinqi Chu, Yuan Qi, Youngja Park

A Simple Homotopy Algorithm for Compressive Sensing

Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

Minimizing Nonconvex Non-Separable Functions

Yaoliang Yu, Xun Zheng, Micol Marchetti-Bowick, Eric Xing

A la Carte – Learning Fast Kernels

Zichao Yang, Andrew Wilson, Alex Smola, Le Song

Majorization-Minimization for Manifold Embedding

Zhirong Yang, Jaakko Peltonen, Samuel Kaski

Stochastic Block Transition Models for Dynamic Networks

Kevin Xu

Understanding and Evaluating Sparse Linear Discriminant Analysis

Yi Wu, David Wipf, Jeong-Min Yun

Revisiting the Limits of MAP Inference by MWSS on Perfect Graphs

Adrian Weller

A Greedy Homotopy Method for Regression with Nonconvex Constraints

Fabian Wauthier, Peter Donnelly

Trend Filtering on Graphs

Yu-Xiang Wang, James Sharpnack, Alex Smola, Ryan Tibshirani

Column Subset Selection with Missing Data via Active Sampling

Yining Wang, Aarti Singh

Multi-Manifold Modeling in Non-Euclidean spaces

Xu Wang, Konstantinos Slavakis, Gilad Lerman

Falling Rule Lists

Fulton Wang, Cynthia Rudin

Maximally Informative Hierarchical Representations of High-Dimensional Data

Greg Ver Steeg, Aram Galstyan

Learning of Non-Parametric Control Policies with High-Dimensional State Features

Herke van Hoof, Jan Peters, Gerhard Neumann

Particle Gibbs with Ancestor Sampling for Probabilistic Programs

Jan-Willem Meent, Hongseok Yang, Vikash Mansinghka, Frank Wood

Missing at Random in Graphical Models

Jin Tian

Streaming Variational Inference for Bayesian Nonparametric Mixture Models

Alex Tank, Nicholas Foti, Emily Fox

Predicting Preference Reversals via Gaussian Process Uncertainty Aversion

Rikiya Takahashi, Tetsuro Morimura

Two-stage sampled learning theory on distributions

Zoltan Szabo, Arthur Gretton, Barnabás Póczos, Bharath Sriperumbudur

Inferring Block Structure of Graphical Models in Exponential Families

Siqi Sun, Hai Wang, Jinbo Xu

A Dirichlet Process Mixture Model for Spherical Data

Julian Straub, Jason Chang, Oren Freifeld, John Fisher III

Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields

Julien Stoehr, Nial Friel

WASP: Scalable Bayes via barycenters of subset posteriors

Sanvesh Srivastava, Volkan Cevher, Quoc Dinh, David Dunson

Data modeling with the elliptical gamma distribution

Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge

Learning from Data with Heterogeneous Noise using SGD

Shuang Song, Kamalika Chaudhuri, Anand Sarwate

State Space Methods for Efficient Inference in Student-t Process Regression

Arno Solin, Simo Särkkä

Learning Where to Sample in Structured Prediction

Tianlin Shi, Jacob Steinhardt, Percy Liang

Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM

Roshan Shariff, András György, Csaba Szepesvári

Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence

Nihar Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran

Inference of Cause and Effect with Unsupervised Inverse Regression

Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf

A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels

Clayton Scott

Sensor Selection for Crowdsensing Dynamical Systems

Francois Schnitzler, Jia Yuan Yu, Shie Mannor

Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields

Mark Schmidt, Reza Babanezhad, Mohamed Ahmed, Aaron Defazio, Ann Clifton

Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation

Hiroaki Sasaki, Yung-kyun Noh, Masashi Sugiyama

Gamma Processes, Stick-Breaking, and Variational Inference

Anirban Roychowdhury, Brian Kulis

Learning Efficient Anomaly Detectors from K-NN Graphs

Jonathan Root, Jing Qian, Venkatesh Saligrama

A Scalable Algorithm for Structured Kernel Feature Selection

Shaogang Ren, Shuai Huang, John Onofrey, Xenios Papademetris, Xiaoning Qian

On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives

Sashank Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry Wasserman

Deep Exponential Families

Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David Blei

Robust sketching for multiple square-root LASSO problems

Vu Pham, Laurent El Ghaoui

On Theoretical Properties of Sum-Product Networks

Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf, Pedro Domingos

Feature Selection for Linear SVM with Provable Guarantees

Saurabh Paul, Malik Magdon-Ismail, Petros Drineas

Reactive bandits with attitude

Pedro Ortega, Kee-eung Kim, Daniel Lee

Fast Function to Function Regression

Junier Oliva, William Neiswanger, Barnabás Póczos, Eric Xing, Hy Trac

Variance Reduction via Antithetic Markov Chains

James Neufeld, Dale Schuurmans, Michael Bowling

Efficient Training of Structured SVMs via Soft Constraints

Ofer Meshi, Nathan Srebro, Tamir Hazan

A Spectral Algorithm for Inference in Hidden semi-Markov Models

Igor Melnyk, Arindam Banerjee

The Security of Latent Dirichlet Allocation

Shike Mei, Xiaojin Zhu

Active Pointillistic Pattern Search

Yifei Ma, Danica J. Sutherland, Roman Garnett, Jeff Schneider

Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning

Mario Lucic, Mesrob Ohannessian, Amin Karbasi, Andreas Krause

Kuan Liu, Aurélien Bellet, Fei Sha

Sparsistency of \ell_1-Regularized M-Estimators

Yen-Huan Li, Jonathan Scarlett, Pradeep Ravikumar, Volkan Cevher

Conditional Restricted Boltzmann Machines for Multi-label Learning with Incomplete Labels

Xin Li, Feipeng Zhao, Yuhong Guo

Max-Margin Zero-Shot Learning for Multi-class Classification

Xin Li, Yuhong Guo

Compressed Sensing with Very Sparse Gaussian Random Projections

Ping Li, Cun-hui Zhang

Toward Minimax Off-policy Value Estimation

Lihong Li, Rémi Munos, Csaba Szepesvári

Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings

Bo Li, Yevgeniy Vorobeychik

Modelling Policies in MDPs in Reproducing Kernel Hilbert Space

Guy Lever, Ronnie Stafford

Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility

Juho Lee, Seungjin Choi

Preferential Attachment in Graphs with Affinities

Jay Lee, Manzil Zaheer, Stephan Günnemann, Alex Smola

Deeply-Supervised Nets

Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, Zhuowen Tu

Particle Gibbs for Bayesian Additive Regression Trees

Balaji Lakshminarayanan, Daniel Roy, Yee Whye Teh

Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering

Simon Lacoste-Julien, Fredrik Lindsten, Francis Bach

Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits

Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvári

Symmetric Iterative Proportional Fitting

Sven Kurras

Low-Rank Spectral Learning with Weighted Loss Functions

Alex Kulesza, Nan Jiang, Satinder Singh

Tensor Factorization via Matrix Factorization

Volodymyr Kuleshov, Arun Chaganty, Percy Liang

On Estimating L_2^2 Divergence

Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry Wasserman

DART: Dropouts meet Multiple Additive Regression Trees

Rashmi Korlakai Vinayak, Ran Gilad-Bachrach

A Bayes consistent 1-NN classifier

Aryeh Kontorovich, Roi Weiss

Dimensionality estimation without distances

Matthäus Kleindessner, Ulrike Luxburg

Latent feature regression for multivariate count data

Arto Klami, Abhishek Tripathi, Johannes Sirola, Lauri Väre, Frederic Roulland

Sparse Submodular Probabilistic PCA

Rajiv Khanna, Joydeep Ghosh, Russell Poldrack, Oluwasanmi Koyejo

On Approximate Non-submodular Minimization via Tree-Structured Supermodularity

Yoshinobu Kawahara, Rishabh Iyer, Jeffrey Bilmes

Robust Cost Sensitive Support Vector Machine

Shuichi Katsumata, Akiko Takeda

Consensus Message Passing for Layered Graphical Models

Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn

Sparse Dueling Bandits

Kevin Jamieson, Sumeet Katariya, Atul Deshpande, Robert Nowak

Estimating the accuracies of multiple classifiers without labeled data

Ariel Jaffe, Boaz Nadler, Yuval Kluger

Online Optimization : Competing with Dynamic Comparators

Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, Karthik Sridharan

Submodular Point Processes with Applications to Machine learning

Rishabh Iyer, Jeffrey Bilmes

Cross-domain recommendation without shared users or items by sharing latent vector distributions

Tomoharu Iwata, Takeuchi Koh

Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process

Michael Hughes, Dae Il Kim, Erik Sudderth

Stochastic Structured Variational Inference

Matthew Hoffman, David Blei

Scalable Variational Gaussian Process Classification

James Hensman, Alexander Matthews, Zoubin Ghahramani

Metric recovery from directed unweighted graphs

Tatsunori Hashimoto, Yi Sun, Tommi Jaakkola

A Consistent Method for Graph Based Anomaly Localization

Satoshi Hara, Tetsuro Morimura, Toshihiro Takahashi, Hiroki Yanagisawa, Taiji Suzuki

Preserving Privacy of Continuous High-dimensional Data with Minimax Filters

Jihun Hamm

The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation

Fangjian Guo, Charles Blundell, Hanna Wallach, Katherine Heller

Consistent Collective Matrix Completion under Joint Low Rank Structure

Suriya Gunasekar, Makoto Yamada, Dawei Yin, Yi Chang

Modeling Skill Acquisition Over Time with Sequence and Topic Modeling

José González-Brenes

On Anomaly Ranking and Excess-Mass Curves

Nicolas Goix, Anne Sabourin, Stéphan Clémençon

Efficient Estimation of Mutual Information for Strongly Dependent Variables

Shuyang Gao, Greg Ver Steeg, Aram Galstyan

Learning Deep Sigmoid Belief Networks with Data Augmentation

Zhe Gan, Ricardo Henao, David Carlson, Lawrence Carin

Computational Complexity of Linear Large Margin Classification With Ramp Loss

Søren Frejstrup Maibing, Christian Igel

A Sufficient Statistics Construction of Exponential Family Lévy Measure Densities for Nonparametric Conjugate Models

Robert Finn, Brian Kulis

Graph Approximation and Clustering on a Budget

Ethan Fetaya, Ohad Shamir, Shimon Ullman