ICML 2013
283 papers
\proptoSVM for Learning with Label Proportions
Felix Yu, Dong Liu, Sanjiv Kumar, Jebara Tony, Shih-Fu Chang A Machine Learning Framework for Programming by Example
Aditya Menon, Omer Tamuz, Sumit Gulwani, Butler Lampson, Adam Kalai A Practical Algorithm for Topic Modeling with Provable Guarantees
Sanjeev Arora, Rong Ge, Yonatan Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, Michael Zhu ABC Reinforcement Learning
Christos Dimitrakakis, Nikolaos Tziortziotis Active Learning for Multi-Objective Optimization
Marcela Zuluaga, Guillaume Sergent, Andreas Krause, Markus Püschel Adaptive Sparsity in Gaussian Graphical Models
Eleanor Wong, Suyash Awate, P. Thomas Fletcher Anytime Representation Learning
Zhixiang Xu, Matt Kusner, Gao Huang, Kilian Weinberger Approximate Inference in Collective Graphical Models
Daniel Sheldon, Tao Sun, Akshat Kumar, Tom Dietterich Bayesian Games for Adversarial Regression Problems
Michael Großhans, Christoph Sawade, Michael Brückner, Tobias Scheffer Better Mixing via Deep Representations
Yoshua Bengio, Gregoire Mesnil, Yann Dauphin, Salah Rifai Block-Coordinate Frank-Wolfe Optimization for Structural SVMs
Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher Characterizing the Representer Theorem
Yaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvari COCO-Q: Learning in Stochastic Games with Side Payments
Eric Sodomka, Elizabeth Hilliard, Michael Littman, Amy Greenwald Collaborative Hyperparameter Tuning
Rémi Bardenet, Mátyás Brendel, Balázs Kégl, Michèle Sebag Concurrent Reinforcement Learning from Customer Interactions
David Silver, Leonard Newnham, David Barker, Suzanne Weller, Jason McFall Consistency of Online Random Forests
Misha Denil, David Matheson, Nando Freitas Cost-Sensitive Multiclass Classification Risk Bounds
Bernardo Ávila Pires, Csaba Szepesvari, Mohammad Ghavamzadeh Cost-Sensitive Tree of Classifiers
Zhixiang Xu, Matt Kusner, Kilian Weinberger, Minmin Chen Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels
Kai Zhang, Vincent Zheng, Qiaojun Wang, James Kwok, Qiang Yang, Ivan Marsic Deep Canonical Correlation Analysis
Galen Andrew, Raman Arora, Jeff Bilmes, Karen Livescu Deep Learning with COTS HPC Systems
Adam Coates, Brody Huval, Tao Wang, David Wu, Bryan Catanzaro, Ng Andrew Dependent Normalized Random Measures
Changyou Chen, Vinayak Rao, Wray Buntine, Yee Whye Teh Discriminatively Activated Sparselets
Ross Girshick, Hyun Oh Song, Trevor Darrell Distribution to Distribution Regression
Junier Oliva, Barnabas Poczos, Jeff Schneider Domain Adaptation Under Target and Conditional Shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang Efficient Ranking from Pairwise Comparisons
Fabian Wauthier, Michael Jordan, Nebojsa Jojic Efficient Semi-Supervised and Active Learning of Disjunctions
Nina Balcan, Christopher Berlind, Steven Ehrlich, Yingyu Liang Ellipsoidal Multiple Instance Learning
Gabriel Krummenacher, Cheng Soon Ong, Joachim Buhmann Exploring the Mind: Integrating Questionnaires and fMRI
Esther Salazar, Ryan Bogdan, Adam Gorka, Ahmad Hariri, Lawrence Carin Fast Dropout Training
Sida Wang, Christopher Manning Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models
Mohammad Emtiyaz Khan, Aleksandr Aravkin, Michael Friedlander, Matthias Seeger Fast Image Tagging
Minmin Chen, Alice Zheng, Kilian Weinberger Fixed-Point Model for Structured Labeling
Quannan Li, Jingdong Wang, David Wipf, Zhuowen Tu Gaussian Process Vine Copulas for Multivariate Dependence
David Lopez-Paz, Jose Miguel Hernández-Lobato, Ghahramani Zoubin Generic Exploration and K-Armed Voting Bandits
Tanguy Urvoy, Fabrice Clerot, Raphael Féraud, Sami Naamane Gossip-Based Distributed Stochastic Bandit Algorithms
Balazs Szorenyi, Robert Busa-Fekete, Istvan Hegedus, Robert Ormandi, Mark Jelasity, Balazs Kegl Guided Policy Search
Sergey Levine, Vladlen Koltun Human Boosting
Harsh Pareek, Pradeep Ravikumar Joint Transfer and Batch-Mode Active Learning
Rita Chattopadhyay, Wei Fan, Ian Davidson, Sethuraman Panchanathan, Jieping Ye Kernelized Bayesian Matrix Factorization
Mehmet Gönen, Suleiman Khan, Samuel Kaski Label Partitioning for Sublinear Ranking
Jason Weston, Ameesh Makadia, Hector Yee Large-Scale Bandit Problems and KWIK Learning
Jacob Abernethy, Kareem Amin, Michael Kearns, Moez Draief Large-Scale Learning with Less RAM via Randomization
Daniel Golovin, D. Sculley, Brendan McMahan, Michael Young Learning Connections in Financial Time Series
Gartheeban Ganeshapillai, John Guttag, Andrew Lo Learning Fair Representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, Cynthia Dwork Learning from Human-Generated Lists
Kwang-Sung Jun, Jerry Zhu, Burr Settles, Timothy Rogers Learning Hash Functions Using Column Generation
Xi Li, Guosheng Lin, Chunhua Shen, Anton Hengel, Anthony Dick Learning Linear Bayesian Networks with Latent Variables
Animashree Anandkumar, Daniel Hsu, Adel Javanmard, Sham Kakade Learning Policies for Contextual Submodular Prediction
Stephane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, Drew Bagnell Learning with Marginalized Corrupted Features
Laurens Maaten, Minmin Chen, Stephen Tyree, Kilian Weinberger Local Low-Rank Matrix Approximation
Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer Max-Margin Multiple-Instance Dictionary Learning
Xinggang Wang, Baoyuan Wang, Xiang Bai, Wenyu Liu, Zhuowen Tu Maxout Networks
Ian Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio Mini-Batch Primal and Dual Methods for SVMs
Martin Takac, Avleen Bijral, Peter Richtarik, Nati Srebro Modeling Information Propagation with Survival Theory
Manuel Gomez-Rodriguez, Jure Leskovec, Bernhard Schölkopf Monochromatic Bi-Clustering
Sharon Wulff, Ruth Urner, Shai Ben-David Multilinear Multitask Learning
Bernardino Romera-Paredes, Hane Aung, Nadia Bianchi-Berthouze, Massimiliano Pontil Multiple Identifications in Multi-Armed Bandits
Séebastian Bubeck, Tengyao Wang, Nitin Viswanathan Multiple-Source Cross-Validation
Krzysztof Geras, Charles Sutton Near-Optimal Bounds for Cross-Validation via Loss Stability
Ravi Kumar, Daniel Lokshtanov, Sergei Vassilvitskii, Andrea Vattani No More Pesky Learning Rates
Tom Schaul, Sixin Zhang, Yann LeCun Non-Linear Stationary Subspace Analysis with Application to Video Classification
Mahsa Baktashmotlagh, Mehrtash Harandi, Abbas Bigdeli, Brian Lovell, Mathieu Salzmann On Autoencoder Scoring
Hanna Kamyshanska, Roland Memisevic On Learning Parametric-Output HMMs
Aryeh Kontorovich, Boaz Nadler, Roi Weiss One-Pass AUC Optimization
Wei Gao, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou Online Learning Under Delayed Feedback
Pooria Joulani, Andras Gyorgy, Csaba Szepesvari Predictable Dual-View Hashing
Mohammad Rastegari, Jonghyun Choi, Shobeir Fakhraei, Daume Hal, Larry Davis Regularization of Neural Networks Using DropConnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, Rob Fergus Robust Regression on MapReduce
Xiangrui Meng, Michael Mahoney Robust Structural Metric Learning
Daryl Lim, Gert Lanckriet, Brian McFee Rounding Methods for Discrete Linear Classification
Yann Chevaleyre, Frédéerick Koriche, Jean-daniel Zucker Safe Policy Iteration
Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello Sequential Bayesian Search
Zheng Wen, Branislav Kveton, Brian Eriksson, Sandilya Bhamidipati Smooth Operators
Steffen Grunewalder, Gretton Arthur, John Shawe-Taylor Sparse Coding for Multitask and Transfer Learning
Andreas Maurer, Massi Pontil, Bernardino Romera-Paredes Sparse PCA Through Low-Rank Approximations
Dimitris Papailiopoulos, Alexandros Dimakis, Stavros Korokythakis Sparse Projections onto the Simplex
Anastasios Kyrillidis, Stephen Becker, Volkan Cevher, Christoph Koch Stable Coactive Learning via Perturbation
Karthik Raman, Thorsten Joachims, Pannaga Shivaswamy, Tobias Schnabel Stochastic Simultaneous Optimistic Optimization
Michal Valko, Alexandra Carpentier, Rémi Munos Tensor Analyzers
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey Hinton The Bigraphical Lasso
Alfredo Kalaitzis, John Lafferty, Neil D. Lawrence, Shuheng Zhou The Extended Parameter Filter
Yusuf Bugra Erol, Lei Li, Bharath Ramsundar, Russell Stuart The Most Generative Maximum Margin Bayesian Networks
Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf Top-K Selection Based on Adaptive Sampling of Noisy Preferences
Robert Busa-Fekete, Balazs Szorenyi, Weiwei Cheng, Paul Weng, Eyke Huellermeier Topic Discovery Through Data Dependent and Random Projections
Weicong Ding, Mohammad Hossein Rohban, Prakash Ishwar, Venkatesh Saligrama Tree-Independent Dual-Tree Algorithms
Ryan Curtin, William March, Parikshit Ram, David Anderson, Alexander Gray, Charles Isbell Vanishing Component Analysis
Roi Livni, David Lehavi, Sagi Schein, Hila Nachliely, Shai Shalev-Shwartz, Amir Globerson