ICML 2013

283 papers

Ellipsoidal Multiple Instance Learning

Gabriel Krummenacher, Cheng Soon Ong, Joachim Buhmann

Forecastable Component Analysis

Georg Goerg

A Variational Approximation for Topic Modeling of Hierarchical Corpora

Do-kyum Kim, Geoffrey Voelker, Lawrence Saul

Thurstonian Boltzmann Machines: Learning from Multiple Inequalities

Truyen Tran, Dinh Phung, Svetha Venkatesh

A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems

Pinghua Gong, Changshui Zhang, Zhaosong Lu, Jianhua Huang, Jieping Ye

Toward Optimal Stratification for Stratified Monte-Carlo Integration

Alexandra Carpentier, Rémi Munos

Stochastic Simultaneous Optimistic Optimization

Michal Valko, Alexandra Carpentier, Rémi Munos

Gaussian Process Vine Copulas for Multivariate Dependence

David Lopez-paz, José Miguel Hernández-Lobato, Ghahramani Zoubin

Mixture of Mutually Exciting Processes for Viral Diffusion

Shuang-hong Yang, Hongyuan Zha

Rounding Methods for Discrete Linear Classification

Yann Chevaleyre, Frédéerick Koriche, Jean-Daniel Zucker

Convex Adversarial Collective Classification

MohamadAli Torkamani, Daniel Lowd

Efficient Semi-supervised and Active Learning of Disjunctions

Nina Balcan, Christopher Berlind, Steven Ehrlich, Yingyu Liang

Constrained fractional set programs and their application in local clustering and community detection

Thomas Bühler, Shyam Sundar Rangapuram, Simon Setzer, Matthias Hein

Robust Structural Metric Learning

Daryl Lim, Gert Lanckriet, Brian McFee

Learning an Internal Dynamics Model from Control Demonstration

Matthew Golub, Steven Chase, Byron Yu

Vanishing Component Analysis

Roi Livni, David Lehavi, Sagi Schein, Hila Nachliely, Shai Shalev-shwartz

Large-Scale Bandit Problems and KWIK Learning

Jacob Abernethy, Kareem Amin, Michael Kearns, Moez Draief

Dynamical Models and tracking regret in online convex programming

Eric Hall, Rebecca Willett

Characterizing the Representer Theorem

Yaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvári

Online Latent Dirichlet Allocation with Infinite Vocabulary

Ke Zhai, Jordan Boyd-Graber

Better Mixing via Deep Representations

Yoshua Bengio, Gregoire Mesnil, Yann Dauphin, Salah Rifai

Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning

Odalric-ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko

Adaptive Task Assignment for Crowdsourced Classification

Chien-Ju Ho, Shahin Jabbari, Jennifer Wortman Vaughan

Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs

K. S. Sesh Kumar, Francis Bach

A Structural SVM Based Approach for Optimizing Partial AUC

Harikrishna Narasimhan, Shivani Agarwal

ELLA: An Efficient Lifelong Learning Algorithm

Paul Ruvolo, Eric Eaton

Online Feature Selection for Model-based Reinforcement Learning

Trung Nguyen, Zhuoru Li, Tomi Silander, Tze Yun Leong

Enhanced statistical rankings via targeted data collection

Braxton Osting, Christoph Brune, Stanley Osher

Efficient Active Learning of Halfspaces: an Aggressive Approach

Alon Gonen, Sivan Sabato, Shai Shalev-shwartz

A Generalized Kernel Approach to Structured Output Learning

Hachem Kadri, Mohammad Ghavamzadeh, Philippe Preux

Active Learning for Multi-Objective Optimization

Marcela Zuluaga, Guillaume Sergent, Andreas Krause, Markus Püschel

Scaling Multidimensional Gaussian Processes using Projected Additive Approximations

Elad Gilboa, Yunus Saatci, John Cunningham

Iterative Learning and Denoising in Convolutional Neural Associative Memories

Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi

General Functional Matrix Factorization Using Gradient Boosting

Tianqi Chen, Hang Li, Qiang Yang, Yong Yu

Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization

Martin Jaggi

Approximation properties of DBNs with binary hidden units and real-valued visible units

Oswin Krause, Asja Fischer, Tobias Glasmachers, Christian Igel

Learning with Marginalized Corrupted Features

Laurens Maaten, Minmin Chen, Stephen Tyree, Kilian Weinberger

A New Frontier of Kernel Design for Structured Data

Kilho Shin

Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method

Taiji Suzuki

Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery

Yudong Chen, Constantine Caramanis

A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning

Arash Afkanpour, András György, Csaba Szepesvári, Michael Bowling

Optimal rates for stochastic convex optimization under Tsybakov noise condition

Aaditya Ramdas, Aarti Singh

Parsing epileptic events using a Markov switching process model for correlated time series

Drausin Wulsin, Emily Fox, Brian Litt

Efficient Dimensionality Reduction for Canonical Correlation Analysis

Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias

Human Boosting

Harsh Pareek, Pradeep Ravikumar

Feature Selection in High-Dimensional Classification

Mladen Kolar, Han Liu

Average Reward Optimization Objective In Partially Observable Domains

Yuri Grinberg, Doina Precup

Adaptive Sparsity in Gaussian Graphical Models

Eleanor Wong, Suyash Awate, P. Thomas Fletcher

Maximum Variance Correction with Application to A* Search

Wenlin Chen, Kilian Weinberger, Yixin Chen

Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model

Min Xiao, Yuhong Guo

Efficient Sparse Group Feature Selection via Nonconvex Optimization

Shuo Xiang, Xiaoshen Tong, Jieping Ye

Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks

Creighton Heaukulani, Zoubin Ghahramani

Learning Optimally Sparse Support Vector Machines

Andrew Cotter, Shai Shalev-shwartz, Nati Srebro

Multiple Identifications in Multi-Armed Bandits

Séebastian Bubeck, Tengyao Wang, Nitin Viswanathan

Learning Linear Bayesian Networks with Latent Variables

Animashree Anandkumar, Daniel Hsu, Adel Javanmard, Sham Kakade

Principal Component Analysis on non-Gaussian Dependent Data

Fang Han, Han Liu

Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization

Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur

Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation

Boqing Gong, Kristen Grauman, Fei Sha

Fixed-Point Model For Structured Labeling

Quannan Li, Jingdong Wang, David Wipf, Zhuowen Tu

The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification

Ofir Pele, Ben Taskar, Amir Globerson, Michael Werman

Discriminatively Activated Sparselets

Ross Girshick, Hyun Oh Song, Trevor Darrell

A Machine Learning Framework for Programming by Example

Aditya Menon, Omer Tamuz, Sumit Gulwani, Butler Lampson, Adam Kalai

Modelling Sparse Dynamical Systems with Compressed Predictive State Representations

William L. Hamilton, Mahdi Milani Fard, Joelle Pineau

Convex formulations of radius-margin based Support Vector Machines

Huyen Do, Alexandros Kalousis

Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization

Yuxin Chen, Andreas Krause

Combinatorial Multi-Armed Bandit: General Framework and Applications

Wei Chen, Yajun Wang, Yang Yuan

Learning Hash Functions Using Column Generation

Xi Li, Guosheng Lin, Chunhua Shen, Anton Hengel, Anthony Dick

Cost-Sensitive Tree of Classifiers

Zhixiang Xu, Matt Kusner, Kilian Weinberger, Minmin Chen

Gibbs Max-Margin Topic Models with Fast Sampling Algorithms

Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang

Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures

James Bergstra, Daniel Yamins, David Cox

Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction

Sébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla

Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models

Sinead Williamson, Avinava Dubey, Eric Xing

Noisy Sparse Subspace Clustering

Yu-Xiang Wang, Huan Xu

Stochastic Alternating Direction Method of Multipliers

Hua Ouyang, Niao He, Long Tran, Alexander Gray

Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes

Ohad Shamir, Tong Zhang

Fast Probabilistic Optimization from Noisy Gradients

Philipp Hennig

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs

Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher

Xiaowei Zhang, Delin Chu

Sparsity-Based Generalization Bounds for Predictive Sparse Coding

Nishant Mehta, Alexander Gray

Near-Optimal Bounds for Cross-Validation via Loss Stability

Ravi Kumar, Daniel Lokshtanov, Sergei Vassilvitskii, Andrea Vattani

A Spectral Learning Approach to Range-Only SLAM

Byron Boots, Geoff Gordon

Domain Generalization via Invariant Feature Representation

Krikamol Muandet, David Balduzzi, Bernhard Schölkopf

An Optimal Policy for Target Localization with Application to Electron Microscopy

Raphael Sznitman, Aurelien Lucchi, Peter Frazier, Bruno Jedynak, Pascal Fua