ICML 2013

283 papers

Quickly Boosting Decision Trees – Pruning Underachieving Features Early

Ron Appel, Thomas Fuchs, Piotr Dollár, Pietro Perona

A Unified Robust Regression Model for Lasso-like Algorithms

Wenzhuo Yang, Huan Xu

Infinite Positive Semidefinite Tensor Factorization for Source Separation of Mixture Signals

Kazuyoshi Yoshii, Ryota Tomioka, Daichi Mochihashi, Masataka Goto

Revisiting the Nystrom method for improved large-scale machine learning

Alex Gittens, Michael Mahoney

Dynamic Covariance Models for Multivariate Financial Time Series

Yue Wu, José Miguel Hernández-Lobato, Ghahramani Zoubin

On Compact Codes for Spatially Pooled Features

Yangqing Jia, Oriol Vinyals, Trevor Darrell

Riemannian Similarity Learning

Li Cheng

Scalable Simple Random Sampling and Stratified Sampling

Xiangrui Meng

Loss-Proportional Subsampling for Subsequent ERM

Paul Mineiro, Nikos Karampatziakis

Parameter Learning and Convergent Inference for Dense Random Fields

Philipp Kraehenbuehl, Vladlen Koltun

\proptoSVM for Learning with Label Proportions

Felix Yu, Dong Liu, Sanjiv Kumar, Jebara Tony, Shih-fu Chang

Temporal Difference Methods for the Variance of the Reward To Go

Aviv Tamar, Dotan Di Castro, Shie Mannor

Local Deep Kernel Learning for Efficient Non-linear SVM Prediction

Cijo Jose, Prasoon Goyal, Parv Aggrwal, Manik Varma

Entropic Affinities: Properties and Efficient Numerical Computation

Max Vladymyrov, Miguel Carreira-Perpinan

That was fast! Speeding up NN search of high dimensional distributions.

Emanuele Coviello, Adeel Mumtaz, Antoni Chan, Gert Lanckriet

Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy

Jean Honorio, Jaakkola Tommi

Non-Linear Stationary Subspace Analysis with Application to Video Classification

Mahsa Baktashmotlagh, Mehrtash Harandi, Abbas Bigdeli, Brian Lovell, Mathieu Salzmann

On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions

Purushottam Kar, Bharath Sriperumbudur, Prateek Jain, Harish Karnick

Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Corrupted Images

Kyunghyun Cho

Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model

Ming Yang, Yingming Li, Zhongfei Zhang

Spectral Compressed Sensing via Structured Matrix Completion

Yuxin Chen, Yuejie Chi

Efficient Multi-label Classification with Many Labels

Wei Bi, James Kwok

A Local Algorithm for Finding Well-Connected Clusters

Zeyuan Allen Zhu, Silvio Lattanzi, Vahab Mirrokni

Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels

Kai Zhang, Vincent Zheng, Qiaojun Wang, James Kwok, Qiang Yang

Learning Heteroscedastic Models by Convex Programming under Group Sparsity

Arnak Dalalyan, Mohamed Hebiri, Katia Meziani, Joseph Salmon

Solving Continuous POMDPs: Value Iteration with Incremental Learning of an Efficient Space Representation

Sebastian Brechtel, Tobias Gindele, Rüdiger Dillmann

Planning by Prioritized Sweeping with Small Backups

Harm Van Seijen, Rich Sutton

Multi-View Clustering and Feature Learning via Structured Sparsity

Hua Wang, Feiping Nie, Heng Huang

No more pesky learning rates

Tom Schaul, Sixin Zhang, Yann LeCun

Hierarchical Tensor Decomposition of Latent Tree Graphical Models

Le Song, Mariya Ishteva, Ankur Parikh, Eric Xing, Haesun Park

Learning Fair Representations

Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, Cynthia Dwork

Unfolding Latent Tree Structures using 4th Order Tensors

Mariya Ishteva, Haesun Park, Le Song

Safe Policy Iteration

Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello

Robust and Discriminative Self-Taught Learning

Hua Wang, Feiping Nie, Heng Huang

Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations

Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon

Top-down particle filtering for Bayesian decision trees

Balaji Lakshminarayanan, Daniel Roy, Yee Whye Teh

Mean Reversion with a Variance Threshold

Marco Cuturi, Alexandre d’Aspremont

Message passing with l1 penalized KL minimization

Yuan Qi, Yandong Guo

Joint Transfer and Batch-mode Active Learning

Rita Chattopadhyay, Wei Fan, Ian Davidson, Sethuraman Panchanathan, Jieping Ye

Fastfood - Computing Hilbert Space Expansions in loglinear time

Quoc Le, Tamas Sarlos, Alexander Smola

The Most Generative Maximum Margin Bayesian Networks

Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf

MAD-Bayes: MAP-based Asymptotic Derivations from Bayes

Tamara Broderick, Brian Kulis, Michael Jordan

Estimating Unknown Sparsity in Compressed Sensing

Miles Lopes

An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source Separation

Nicholas Bryan, Gautham Mysore

Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning

Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Rich Zemel

A Fast and Exact Energy Minimization Algorithm for Cycle MRFs

Huayan Wang, Koller Daphne

Learning from Human-Generated Lists

Kwang-Sung Jun, Jerry Zhu, Burr Settles, Timothy Rogers

Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression

Toby Hocking, Guillem Rigaill, Jean-philippe Vert, Francis Bach

Tensor Analyzers

Yichuan Tang, Ruslan Salakhutdinov, Geoffrey Hinton

One-Bit Compressed Sensing: Provable Support and Vector Recovery

Sivakant Gopi, Praneeth Netrapalli, Prateek Jain, Aditya Nori

Inference algorithms for pattern-based CRFs on sequence data

Rustem Takhanov, Vladimir Kolmogorov

Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller Space

Javier Almingol, Lui Montesano, Manuel Lopes

Thompson Sampling for Contextual Bandits with Linear Payoffs

Shipra Agrawal, Navin Goyal

Differentially Private Learning with Kernels

Prateek Jain, Abhradeep Thakurta

Efficient Ranking from Pairwise Comparisons

Fabian Wauthier, Michael Jordan, Nebojsa Jojic

Learning invariant features by harnessing the aperture problem

Roland Memisevic, Georgios Exarchakis

Guaranteed Sparse Recovery under Linear Transformation

Ji Liu, Lei Yuan, Jieping Ye

MILEAGE: Multiple Instance LEArning with Global Embedding

Dan Zhang, Jingrui He, Luo Si, Richard Lawrence

Markov Network Estimation From Multi-attribute Data

Mladen Kolar, Han Liu, Eric Xing

Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing

Xi Chen, Qihang Lin, Dengyong Zhou

Bayesian Games for Adversarial Regression Problems

Michael Großhans, Christoph Sawade, Michael Brückner, Tobias Scheffer

Multi-Class Classification with Maximum Margin Multiple Kernel

Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh

Hierarchical Regularization Cascade for Joint Learning

Alon Zweig, Daphna Weinshall

The Sample-Complexity of General Reinforcement Learning

Tor Lattimore, Marcus Hutter, Peter Sunehag

Gossip-based distributed stochastic bandit algorithms

Balázs Szörényi, Róbert Busa-Fekete, Istvan Hegedus, Robert Ormandi, Márk Jelasity

Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning

Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama

Guided Policy Search

Sergey Levine, Vladlen Koltun

Activized Learning with Uniform Classification Noise

Liu Yang, Steve Hanneke

Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data

Jan-Willem Meent, Jonathan Bronson, Frank Wood, Ruben Gonzalez Jr., Chris Wiggins

Direct Modeling of Complex Invariances for Visual Object Features

Ka Yu Hui

Sparse coding for multitask and transfer learning

Andreas Maurer, Massi Pontil, Bernardino Romera-Paredes

Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization

Stefano Ermon, Carla Gomes, Ashish Sabharwal, Bart Selman

Large-Scale Learning with Less RAM via Randomization

Daniel Golovin, D. Sculley, Brendan Mcmahan, Michael Young

Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment

Billy Chang, Uwe Kruger, Rafal Kustra, Junping Zhang

Margins, Shrinkage, and Boosting

Matus Telgarsky

An Adaptive Learning Rate for Stochastic Variational Inference

Rajesh Ranganath, Chong Wang, Blei David, Eric Xing

Distributed training of Large-scale Logistic models

Siddharth Gopal, Yiming Yang

A Practical Algorithm for Topic Modeling with Provable Guarantees

Sanjeev Arora, Rong Ge, Yonatan Halpern, David Mimno, Ankur Moitra

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Quoc Tran Dinh, Anastasios Kyrillidis, Volkan Cevher

Exploring the Mind: Integrating Questionnaires and fMRI

Esther Salazar, Ryan Bogdan, Adam Gorka, Ahmad Hariri, Lawrence Carin

Subtle Topic Models and Discovering Subtly Manifested Software Concerns Automatically

Mrinal Das, Suparna Bhattacharya, Chiranjib Bhattacharyya, Gopinath Kanchi

Modeling Musical Influence with Topic Models

Uri Shalit, Daphna Weinshall, Gal Chechik

Sparse projections onto the simplex

Anastasios Kyrillidis, Stephen Becker, Volkan Cevher, Christoph Koch

Sequential Bayesian Search

Zheng Wen, Branislav Kveton, Brian Eriksson, Sandilya Bhamidipati

Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines

Kihyuk Sohn, Guanyu Zhou, Chansoo Lee, Honglak Lee

SADA: A General Framework to Support Robust Causation Discovery

Ruichu Cai, Zhenjie Zhang, Zhifeng Hao

Collaborative hyperparameter tuning

Rémi Bardenet, Mátyás Brendel, Balázs Kégl, Michele Sebag

Subproblem-Tree Calibration: A Unified Approach to Max-Product Message Passing

Huayan Wang, Koller Daphne

Label Partitioning For Sublinear Ranking

Jason Weston, Ameesh Makadia, Hector Yee

Transition Matrix Estimation in High Dimensional Time Series

Fang Han, Han Liu

Strict Monotonicity of Sum of Squares Error and Normalized Cut in the Lattice of Clusterings

Nicola Rebagliati

Gated Autoencoders with Tied Input Weights

Droniou Alain, Sigaud Olivier

Monochromatic Bi-Clustering

Sharon Wulff, Ruth Urner, Shai Ben-David

Precision-recall space to correct external indices for biclustering

Blaise Hanczar, Mohamed NADIF

Scalable Optimization of Neighbor Embedding for Visualization

Zhirong Yang, Jaakko Peltonen, Samuel Kaski

Fast dropout training

Sida Wang, Christopher Manning

Learning Connections in Financial Time Series

Gartheeban Ganeshapillai, John Guttag, Andrew Lo

A unifying framework for vector-valued manifold regularization and multi-view learning

Minh Ha Quang, Loris Bazzani, Vittorio Murino

Generic Exploration and K-armed Voting Bandits

Tanguy Urvoy, Fabrice Clérot, Raphaël Féraud, Sami Naamane

Local Low-Rank Matrix Approximation

Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer