ICML 2015

270 papers

Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs

Yarin Gal, Richard Turner

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data

Yarin Gal, Yutian Chen, Zoubin Ghahramani

Learning Parametric-Output HMMs with Two Aliased States

Roi Weiss, Boaz Nadler

On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence

Nathaniel Korda, Prashanth La

Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares

Garvesh Raskutti, Michael Mahoney

Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods

Seth Flaxman, Andrew Wilson, Daniel Neill, Hannes Nickisch, Alex Smola

Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network

Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han

Ordinal Mixed Membership Models

Seppo Virtanen, Mark Girolami

Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup

Yufei Ding, Yue Zhao, Xipeng Shen, Madanlal Musuvathi, Todd Mytkowicz

A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data

Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low

Online Learning of Eigenvectors

Dan Garber, Elad Hazan, Tengyu Ma

Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models

Mrinal Das, Trapit Bansal, Chiranjib Bhattacharyya

Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets

Dan Garber, Elad Hazan

The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling

Michael Betancourt

Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning

K. Lakshmanan, Ronald Ortner, Daniil Ryabko

How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances?

Senjian An, Farid Boussaid, Mohammed Bennamoun

A Divide and Conquer Framework for Distributed Graph Clustering

Wenzhuo Yang, Huan Xu

Streaming Sparse Principal Component Analysis

Wenzhuo Yang, Huan Xu

A Unified Framework for Outlier-Robust PCA-like Algorithms

Wenzhuo Yang, Huan Xu

Markov Mixed Membership Models

Aonan Zhang, John Paisley

Landmarking Manifolds with Gaussian Processes

Dawen Liang, John Paisley

Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds

Yuchen Zhang, Martin Wainwright, Michael Jordan

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Sergey Ioffe, Christian Szegedy

Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains

Katharina Blechschmidt, Joachim Giesen, Soeren Laue

Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View

Takanori Maehara, Akihiro Yabe, Ken-Ichi Kawarabayashi

Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA

Bo Xin, David Wipf

The Benefits of Learning with Strongly Convex Approximate Inference

Ben London, Bert Huang, Lise Getoor

A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning

Debarghya Ghoshdastidar, Ambedkar Dukkipati

Structural Maxent Models

Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Umar Syed

Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs

Stephen Bach, Bert Huang, Jordan Boyd-Graber, Lise Getoor

Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons

Yuxin Chen, Changho Suh

DiSCO: Distributed Optimization for Self-Concordant Empirical Loss

Yuchen Zhang, Xiao Lin

Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization

Yuchen Zhang, Xiao Lin

A General Analysis of the Convergence of ADMM

Robert Nishihara, Laurent Lessard, Ben Recht, Andrew Packard, Michael Jordan

Mind the duality gap: safer rules for the Lasso

Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data

Toby Hocking, Guillem Rigaill, Guillaume Bourque

Generalization error bounds for learning to rank: Does the length of document lists matter?

Ambuj Tewari, Sougata Chaudhuri

Theory of Dual-sparse Regularized Randomized Reduction

Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu

High Dimensional Bayesian Optimisation and Bandits via Additive Models

Kirthevasan Kandasamy, Jeff Schneider, Barnabás Póczos

Telling cause from effect in deterministic linear dynamical systems

Naji Shajarisales, Dominik Janzing, Bernhard Schoelkopf, Michel Besserve

A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis

Pinghua Gong, Jieping Ye

Dynamic Sensing: Better Classification under Acquisition Constraints

Oran Richman, Shie Mannor

Classification with Low Rank and Missing Data

Elad Hazan, Roi Livni, Yishay Mansour

Atomic Spatial Processes

Sean Jewell, Neil Spencer, Alexandre Bouchard-côté

Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams

Rose Yu, Dehua Cheng, Yan Liu

Functional Subspace Clustering with Application to Time Series

Mohammad Taha Bahadori, David Kale, Yingying Fan, Yan Liu

A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits

Pratik Gajane, Tanguy Urvoy, Fabrice Clérot

Coresets for Nonparametric Estimation - the Case of DP-Means

Olivier Bachem, Mario Lucic, Andreas Krause

Optimizing Non-decomposable Performance Measures: A Tale of Two Classes

Harikrishna Narasimhan, Purushottam Kar, Prateek Jain

Surrogate Functions for Maximizing Precision at the Top

Purushottam Kar, Harikrishna Narasimhan, Prateek Jain

Abstraction Selection in Model-based Reinforcement Learning

Nan Jiang, Alex Kulesza, Satinder Singh

Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis

Zhuang Ma, Yichao Lu, Dean Foster

Learning Local Invariant Mahalanobis Distances

Ethan Fetaya, Shimon Ullman

Attribute Efficient Linear Regression with Distribution-Dependent Sampling

Doron Kukliansky, Ohad Shamir

A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate

Ohad Shamir

An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection

Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu

Learning from Corrupted Binary Labels via Class-Probability Estimation

Aditya Menon, Brendan van Rooyen, Cheng Soon Ong, Bob Williamson

On the Relationship between Sum-Product Networks and Bayesian Networks

Han Zhao, Mazen Melibari, Pascal Poupart

Robust partially observable Markov decision process

Takayuki Osogami

Learning Transferable Features with Deep Adaptation Networks

Mingsheng Long, Yue Cao, Jianmin Wang, Michael Jordan

Learning Word Representations with Hierarchical Sparse Coding

Dani Yogatama, Manaal Faruqui, Chris Dyer, Noah Smith

A Lower Bound for the Optimization of Finite Sums

Alekh Agarwal, Léon Bottou

Adaptive Stochastic Alternating Direction Method of Multipliers

Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li

Efficient Training of LDA on a GPU by Mean-for-Mode Estimation

Jean-Baptiste Tristan, Joseph Tassarotti, Guy Steele

Information Geometry and Minimum Description Length Networks

Ke Sun, Jun Wang, Alexandros Kalousis, Stephan Marchand-Maillet

Spectral Clustering via the Power Method - Provably

Christos Boutsidis, Prabhanjan Kambadur, Alex Gittens

An Aligned Subtree Kernel for Weighted Graphs

Lu Bai, Luca Rossi, Zhihong Zhang, Edwin Hancock

A low variance consistent test of relative dependency

Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew Blaschko

Approval Voting and Incentives in Crowdsourcing

Nihar Shah, Dengyong Zhou, Yuval Peres

Stochastic Optimization with Importance Sampling for Regularized Loss Minimization

Peilin Zhao, Tong Zhang