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ORCID30th ICML 2013: Atlanta, GA, USA
- Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013. JMLR Workshop and Conference Proceedings 28, JMLR.org 2013
Cycle 1 Papers
- Raphael Sznitman, Aurélien Lucchi, Peter I. Frazier, Bruno Jedynak, Pascal Fua:
An Optimal Policy for Target Localization with Application to Electron Microscopy. 1-9 - Krikamol Muandet, David Balduzzi, Bernhard Schölkopf:
Domain Generalization via Invariant Feature Representation. 10-18 - Byron Boots, Geoffrey J. Gordon:
A Spectral Learning Approach to Range-Only SLAM. 19-26 - Ravi Kumar, Daniel Lokshtanov, Sergei Vassilvitskii, Andrea Vattani:
Near-Optimal Bounds for Cross-Validation via Loss Stability. 27-35 - Nishant Ajay Mehta, Alexander G. Gray:
Sparsity-Based Generalization Bounds for Predictive Sparse Coding. 36-44 - Xiaowei Zhang, Delin Chu:
Sparse Uncorrelated Linear Discriminant Analysis. 45-52 - Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher:
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. 53-61 - Philipp Hennig:
Fast Probabilistic Optimization from Noisy Gradients. 62-70 - Ohad Shamir, Tong Zhang:
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes. 71-79 - Hua Ouyang, Niao He, Long Q. Tran, Alexander G. Gray:
Stochastic Alternating Direction Method of Multipliers. 80-88 - Yu-Xiang Wang, Huan Xu:
Noisy Sparse Subspace Clustering. 89-97 - Sinead Williamson, Avinava Dubey, Eric P. Xing:
Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models. 98-106 - Sébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla:
Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction. 107-114 - James Bergstra, Daniel Yamins, David D. Cox:
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. 115-123 - Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang:
Gibbs Max-Margin Topic Models with Fast Sampling Algorithms. 124-132 - Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen:
Cost-Sensitive Tree of Classifiers. 133-141 - Xi Li, Guosheng Lin, Chunhua Shen, Anton van den Hengel, Anthony R. Dick:
Learning Hash Functions Using Column Generation. 142-150 - Wei Chen, Yajun Wang, Yang Yuan:
Combinatorial Multi-Armed Bandit: General Framework and Applications. 151-159 - Yuxin Chen, Andreas Krause:
Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization. 160-168 - Huyen Do, Alexandros Kalousis:
Convex formulations of radius-margin based Support Vector Machines. 169-177 - William L. Hamilton, Mahdi Milani Fard, Joelle Pineau:
Modelling Sparse Dynamical Systems with Compressed Predictive State Representations. 178-186 - Aditya Krishna Menon, Omer Tamuz, Sumit Gulwani, Butler W. Lampson, Adam Kalai:
A Machine Learning Framework for Programming by Example. 187-195 - Ross B. Girshick, Hyun Oh Song, Trevor Darrell:
Discriminatively Activated Sparselets. 196-204 - Ofir Pele, Ben Taskar, Amir Globerson, Michael Werman:
The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification. 205-213 - Quannan Li, Jingdong Wang, David P. Wipf, Zhuowen Tu:
Fixed-Point Model For Structured Labeling. 214-221 - Boqing Gong, Kristen Grauman, Fei Sha:
Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation. 222-230 - Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur:
Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization. 231-239 - Fang Han, Han Liu:
Principal Component Analysis on non-Gaussian Dependent Data. 240-248 - Animashree Anandkumar, Daniel J. Hsu, Adel Javanmard, Sham M. Kakade:
Learning Linear Bayesian Networks with Latent Variables. 249-257 - Sébastien Bubeck, Tengyao Wang, Nitin Viswanathan:
Multiple Identifications in Multi-Armed Bandits. 258-265 - Andrew Cotter, Shai Shalev-Shwartz, Nati Srebro:
Learning Optimally Sparse Support Vector Machines. 266-274 - Creighton Heaukulani, Zoubin Ghahramani:
Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks. 275-283 - Shuo Xiang, Xiaoshen Tong, Jieping Ye:
Efficient Sparse Group Feature Selection via Nonconvex Optimization. 284-292 - Min Xiao, Yuhong Guo:
Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model. 293-301 - Wenlin Chen, Kilian Q. Weinberger, Yixin Chen:
Maximum Variance Correction with Application to A* Search. 302-310 - Eleanor Wong, Suyash P. Awate, P. Thomas Fletcher:
Adaptive Sparsity in Gaussian Graphical Models. 311-319 - Yuri Grinberg, Doina Precup:
Average Reward Optimization Objective In Partially Observable Domains. 320-328 - Mladen Kolar, Han Liu:
Feature Selection in High-Dimensional Classification. 329-337 - Harsh H. Pareek, Pradeep Ravikumar:
Human Boosting. 338-346 - Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias:
Efficient Dimensionality Reduction for Canonical Correlation Analysis. 347-355 - Drausin Wulsin, Emily B. Fox, Brian Litt:
Parsing epileptic events using a Markov switching process model for correlated time series. 356-364 - Aaditya Ramdas, Aarti Singh:
Optimal rates for stochastic convex optimization under Tsybakov noise condition. 365-373 - Arash Afkanpour, András György, Csaba Szepesvári, Michael Bowling:
A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning. 374-382 - Yudong Chen, Constantine Caramanis:
Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery. 383-391 - Taiji Suzuki:
Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method. 392-400 - Kilho Shin:
A New Frontier of Kernel Design for Structured Data. 401-409 - Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Q. Weinberger:
Learning with Marginalized Corrupted Features. 410-418 - Oswin Krause, Asja Fischer, Tobias Glasmachers, Christian Igel:
Approximation properties of DBNs with binary hidden units and real-valued visible units. 419-426 - Martin Jaggi:
Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization. 427-435 - Tianqi Chen, Hang Li, Qiang Yang, Yong Yu:
General Functional Matrix Factorization Using Gradient Boosting. 436-444 - Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi:
Iterative Learning and Denoising in Convolutional Neural Associative Memories. 445-453 - Elad Gilboa, Yunus Saatçi, John P. Cunningham:
Scaling Multidimensional Gaussian Processes using Projected Additive Approximations. 454-461 - Marcela Zuluaga, Guillaume Sergent, Andreas Krause, Markus Püschel:
Active Learning for Multi-Objective Optimization. 462-470 - Hachem Kadri, Mohammad Ghavamzadeh, Philippe Preux:
A Generalized Kernel Approach to Structured Output Learning. 471-479 - Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz:
Efficient Active Learning of Halfspaces: an Aggressive Approach. 480-488 - Braxton Osting, Christoph Brune, Stanley J. Osher:
Enhanced statistical rankings via targeted data collection. 489-497 - Trung Thanh Nguyen, Zhuoru Li, Tomi Silander, Tze-Yun Leong:
Online Feature Selection for Model-based Reinforcement Learning. 498-506 - Paul Ruvolo, Eric Eaton:
ELLA: An Efficient Lifelong Learning Algorithm. 507-515 - Harikrishna Narasimhan, Shivani Agarwal:
A Structural SVM Based Approach for Optimizing Partial AUC. 516-524 - K. S. Sesh Kumar, Francis R. Bach:
Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs. 525-533 - Chien-Ju Ho, Shahin Jabbari, Jennifer Wortman Vaughan:
Adaptive Task Assignment for Crowdsourced Classification. 534-542 - Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko:
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning. 543-551 - Yoshua Bengio, Grégoire Mesnil, Yann N. Dauphin, Salah Rifai:
Better Mixing via Deep Representations. 552-560 - Ke Zhai, Jordan L. Boyd-Graber:
Online Latent Dirichlet Allocation with Infinite Vocabulary. 561-569 - Yaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvári:
Characterizing the Representer Theorem. 570-578 - Eric C. Hall, Rebecca Willett:
Dynamical Models and tracking regret in online convex programming. 579-587 - Jacob D. Abernethy, Kareem Amin, Michael J. Kearns, Moez Draief:
Large-Scale Bandit Problems and KWIK Learning. 588-596 - Roi Livni, David Lehavi, Sagi Schein, Hila Nachlieli, Shai Shalev-Shwartz, Amir Globerson:
Vanishing Component Analysis. 597-605 - Matthew D. Golub, Steven M. Chase, Byron M. Yu:
Learning an Internal Dynamics Model from Control Demonstration. 606-614 - Daryl Lim, Gert R. G. Lanckriet, Brian McFee:
Robust Structural Metric Learning. 615-623 - Thomas Bühler, Syama Sundar Rangapuram, Simon Setzer, Matthias Hein:
Constrained fractional set programs and their application in local clustering and community detection. 624-632 - Nina Balcan, Christopher Berlind, Steven Ehrlich, Yingyu Liang:
Efficient Semi-supervised and Active Learning of Disjunctions. 633-641 - MohamadAli Torkamani, Daniel Lowd:
Convex Adversarial Collective Classification. 642-650 - Yann Chevaleyre, Frédéric Koriche, Jean-Daniel Zucker:
Rounding Methods for Discrete Linear Classification. 651-659
Cycle 2 Papers
- Shuang-Hong Yang, Hongyuan Zha:
Mixture of Mutually Exciting Processes for Viral Diffusion. 1-9 - David Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Gaussian Process Vine Copulas for Multivariate Dependence. 10-18 - Michal Valko, Alexandra Carpentier, Rémi Munos:
Stochastic Simultaneous Optimistic Optimization. 19-27 - Alexandra Carpentier, Rémi Munos:
Toward Optimal Stratification for Stratified Monte-Carlo Integration. 28-36 - Pinghua Gong, Changshui Zhang, Zhaosong Lu, Jianhua Huang, Jieping Ye:
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems. 37-45 - Truyen Tran, Dinh Q. Phung, Svetha Venkatesh:
Thurstonian Boltzmann Machines: Learning from Multiple Inequalities. 46-54 - Do-kyum Kim, Geoffrey M. Voelker, Lawrence K. Saul:
A Variational Approximation for Topic Modeling of Hierarchical Corpora. 55-63 - Georg M. Goerg:
Forecastable Component Analysis. 64-72 - Gabriel Krummenacher, Cheng Soon Ong, Joachim M. Buhmann:
Ellipsoidal Multiple Instance Learning. 73-81 - Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer:
Local Low-Rank Matrix Approximation. 82-90 - Tanguy Urvoy, Fabrice Clérot, Raphaël Féraud, Sami Naamane:
Generic Exploration and K-armed Voting Bandits. 91-99 - Ha Quang Minh, Loris Bazzani, Vittorio Murino:
A unifying framework for vector-valued manifold regularization and multi-view learning. 100-108 - Gartheeban Ganeshapillai, John V. Guttag, Andrew Lo:
Learning Connections in Financial Time Series. 109-117 - Sida Wang, Christopher D. Manning:
Fast dropout training. 118-126 - Zhirong Yang, Jaakko Peltonen, Samuel Kaski:
Scalable Optimization of Neighbor Embedding for Visualization. 127-135 - Blaise Hanczar, Mohamed Nadif:
Precision-recall space to correct external indices for biclustering. 136-144 - Sharon Wulff, Ruth Urner, Shai Ben-David:
Monochromatic Bi-Clustering. 145-153 - Alain Droniou, Olivier Sigaud:
Gated Autoencoders with Tied Input Weights. 154-162 - Nicola Rebagliati:
Strict Monotonicity of Sum of Squares Error and Normalized Cut in the Lattice of Clusterings. 163-171 - Fang Han, Han Liu:
Transition Matrix Estimation in High Dimensional Time Series. 172-180 - Jason Weston, Ameesh Makadia, Hector Yee:
Label Partitioning For Sublinear Ranking. 181-189 - Huayan Wang, Daphne Koller:
Subproblem-Tree Calibration: A Unified Approach to Max-Product Message Passing. 190-198 - Rémi Bardenet, Mátyás Brendel, Balázs Kégl, Michèle Sebag:
Collaborative hyperparameter tuning. 199-207 - Ruichu Cai, Zhenjie Zhang, Zhifeng Hao:
SADA: A General Framework to Support Robust Causation Discovery. 208-216 - Kihyuk Sohn, Guanyu Zhou, Chansoo Lee, Honglak Lee:
Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines. 217-225 - Zheng Wen, Branislav Kveton, Brian Eriksson, Sandilya Bhamidipati:
Sequential Bayesian Search. 226-234 - Anastasios Kyrillidis, Stephen Becker, Volkan Cevher, Christoph Koch:
Sparse projections onto the simplex. 235-243 - Uri Shalit, Daphna Weinshall, Gal Chechik:
Modeling Musical Influence with Topic Models. 244-252 - Mrinal Kanti Das, Suparna Bhattacharya, Chiranjib Bhattacharyya, Kanchi Gopinath:
Subtle Topic Models and Discovering Subtly Manifested Software Concerns Automatically. 253-261 - Esther Salazar, Ryan Bogdan, Adam Gorka, Ahmad Hariri, Lawrence Carin:
Exploring the Mind: Integrating Questionnaires and fMRI. 262-270 - Quoc Tran-Dinh, Anastasios Kyrillidis, Volkan Cevher:
A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions. 271-279 - Sanjeev Arora, Rong Ge, Yonatan Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu:
A Practical Algorithm for Topic Modeling with Provable Guarantees. 280-288 - Siddharth Gopal, Yiming Yang:
Distributed training of Large-scale Logistic models. 289-297 - Rajesh Ranganath, Chong Wang, David M. Blei, Eric P. Xing:
An Adaptive Learning Rate for Stochastic Variational Inference. 298-306 - Matus Telgarsky:
Margins, Shrinkage, and Boosting. 307-315 - Billy Chang, Uwe Krüger, Rafal Kustra, Junping Zhang:
Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment. 316-324 - Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young:
Large-Scale Learning with Less RAM via Randomization. 325-333 - Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization. 334-342 - Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes:
Sparse coding for multitask and transfer learning. 343-351 - Ka Yu Hui:
Direct Modeling of Complex Invariances for Visual Object Features. 352-360 - Jan-Willem van de Meent, Jonathan E. Bronson, Frank D. Wood, Ruben L. Gonzalez, Chris Wiggins:
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data. 361-369 - Liu Yang, Steve Hanneke:
Activized Learning with Uniform Classification Noise. 370-378
Cycle 3 Papers
- Sergey Levine, Vladlen Koltun:
Guided Policy Search. 1-9 - Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama:
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. 10-18 - Balázs Szörényi, Róbert Busa-Fekete, István Hegedüs, Róbert Ormándi, Márk Jelasity, Balázs Kégl:
Gossip-based distributed stochastic bandit algorithms. 19-27 - Tor Lattimore, Marcus Hutter, Peter Sunehag:
The Sample-Complexity of General Reinforcement Learning. 28-36 - Alon Zweig, Daphna Weinshall:
Hierarchical Regularization Cascade for Joint Learning. 37-45 - Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh:
Multi-Class Classification with Maximum Margin Multiple Kernel. 46-54 - Michael Großhans, Christoph Sawade, Michael Brückner, Tobias Scheffer:
Bayesian Games for Adversarial Regression Problems. 55-63 - Xi Chen, Qihang Lin, Dengyong Zhou:
Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing. 64-72 - Mladen Kolar, Han Liu, Eric P. Xing:
Markov Network Estimation From Multi-attribute Data. 73-81 - Dan Zhang, Jingrui He, Luo Si, Richard D. Lawrence:
MILEAGE: Multiple Instance LEArning with Global Embedding. 82-90 - Ji Liu, Lei Yuan, Jieping Ye:
Guaranteed Sparse Recovery under Linear Transformation. 91-99 - Roland Memisevic, Georgios Exarchakis:
Learning invariant features by harnessing the aperture problem. 100-108 - Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic:
Efficient Ranking from Pairwise Comparisons. 109-117 - Prateek Jain, Abhradeep Thakurta:
Differentially Private Learning with Kernels. 118-126 - Shipra Agrawal, Navin Goyal:
Thompson Sampling for Contextual Bandits with Linear Payoffs. 127-135 - Javier Almingol, Luis Montesano, Manuel Lopes
:
Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller Space. 136-144 - Rustem Takhanov, Vladimir Kolmogorov:
Inference algorithms for pattern-based CRFs on sequence data. 145-153 - Sivakanth Gopi, Praneeth Netrapalli, Prateek Jain, Aditya V. Nori:
One-Bit Compressed Sensing: Provable Support and Vector Recovery. 154-162 - Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton:
Tensor Analyzers. 163-171