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28th ICML 2011: Bellevue, Washington, USA
- Lise Getoor, Tobias Scheffer:
Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011. Omnipress 2011 - Wei Liu, Jun Wang, Sanjiv Kumar, Shih-Fu Chang:
Hashing with Graphs. 1-8 - Wenliang Zhong, James T. Kwok:
Efficient Sparse Modeling with Automatic Feature Grouping. 9-16 - Wei Bi, James T. Kwok:
MultiLabel Classification on Tree- and DAG-Structured Hierarchies. 17-24 - Jingrui He, Rick Lawrence:
A Graphbased Framework for Multi-Task Multi-View Learning. 25-32 - Tianyi Zhou, Dacheng Tao:
GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case. 33-40 - Jia Yuan Yu, Shie Mannor:
Unimodal Bandits. 41-48 - Francesco Dinuzzo, Cheng Soon Ong, Peter V. Gehler, Gianluigi Pillonetto:
Learning Output Kernels with Block Coordinate Descent. 49-56 - Ha Quang Minh, Vikas Sindhwani:
Vector-valued Manifold Regularization. 57-64 - Masashi Sugiyama, Makoto Yamada, Manabu Kimura, Hirotaka Hachiya:
On Information-Maximization Clustering: Tuning Parameter Selection and Analytic Solution. 65-72 - Richard Nock, Brice Magdalou, Eric Briys, Frank Nielsen:
On tracking portfolios with certainty equivalents on a generalization of Markowitz model: the Fool, the Wise and the Adaptive. 73-80 - Boris Babenko, Nakul Verma, Piotr Dollár, Serge J. Belongie:
Multiple Instance Learning with Manifold Bags. 81-88 - Yi Jiang, Jiangtao Ren:
Eigenvalue Sensitive Feature Selection. 89-96 - Jiang Su, Jelber Sayyad Shirab, Stan Matwin:
Large Scale Text Classification using Semisupervised Multinomial Naive Bayes. 97-104 - KyungHyun Cho, Tapani Raiko, Alexander Ilin:
Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines. 105-112 - Daniel Tarlow, Dhruv Batra, Pushmeet Kohli, Vladimir Kolmogorov:
Dynamic Tree Block Coordinate Ascent. 113-120 - Michael W. Mahoney, Lorenzo Orecchia:
Implementing regularization implicitly via approximate eigenvector computation. 121-128 - Richard Socher, Cliff Chiung-Yu Lin, Andrew Y. Ng, Christopher D. Manning:
Parsing Natural Scenes and Natural Language with Recursive Neural Networks. 129-136 - Philip S. Thomas, Andrew G. Barto:
Conjugate Markov Decision Processes. 137-144 - Tyler Lu, Craig Boutilier:
Learning Mallows Models with Pairwise Preferences. 145-152 - Clayton Scott:
Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costs. 153-160 - Pratik Jawanpuria, Jagarlapudi Saketha Nath, Ganesh Ramakrishnan:
Efficient Rule Ensemble Learning using Hierarchical Kernels. 161-168 - André F. T. Martins, Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing:
An Augmented Lagrangian Approach to Constrained MAP Inference. 169-176 - Shie Mannor, John N. Tsitsiklis:
Mean-Variance Optimization in Markov Decision Processes. 177-184 - Lei Li, B. Aditya Prakash:
Time Series Clustering: Complex is Simpler! 185-192 - Stephen Gould:
Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields. 193-200 - Alexander Clark:
Inference of Inversion Transduction Grammars. 201-208 - Enliang Hu, Bo Wang, Songcan Chen:
BCDNPKL: Scalable Non-Parametric Kernel Learning Using Block Coordinate Descent. 209-216 - Laurens van der Maaten:
Learning Discriminative Fisher Kernels. 217-224 - Samory Kpotufe, Ulrike von Luxburg:
Pruning nearest neighbor cluster trees. 225-232 - Peilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbao Yang:
Online AUC Maximization. 233-240 - Yisong Yue, Thorsten Joachims:
Beat the Mean Bandit. 241-248 - Francesco Orabona, Jie Luo:
Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning. 249-256 - Brian Potetz:
Estimating the Bayes Point Using Linear Knapsack Problems. 257-264 - Quoc V. Le, Jiquan Ngiam, Adam Coates, Ahbik Lahiri, Bobby Prochnow, Andrew Y. Ng:
On optimization methods for deep learning. 265-272 - Koby Crammer, Claudio Gentile:
Multiclass Classification with Bandit Feedback using Adaptive Regularization. 273-280 - David P. Helmbold, Philip M. Long:
On the Necessity of Irrelevant Variables. 281-288 - Simon Barthelmé, Nicolas Chopin:
ABC-EP: Expectation Propagation for Likelihoodfree Bayesian Computation. 289-296 - Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian:
A PAC-Bayes Sample-compression Approach to Kernel Methods. 297-304 - Aviv Tamar, Dotan Di Castro, Ron Meir:
Integrating Partial Model Knowledge in Model Free RL Algorithms. 305-312 - Álvaro Barbero Jiménez, Suvrit Sra:
Fast Newton-type Methods for Total Variation Regularization. 313-320 - Joseph K. Bradley, Aapo Kyrola, Danny Bickson, Carlos Guestrin:
Parallel Coordinate Descent for L1-Regularized Loss Minimization. 321-328 - Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir:
Large-Scale Convex Minimization with a Low-Rank Constraint. 329-336 - Lauren Hannah, David B. Dunson:
Approximate Dynamic Programming for Storage Problems. 337-344 - Stefanie Jegelka, Jeff A. Bilmes:
Online Submodular Minimization for Combinatorial Structures. 345-352 - Mohammad Norouzi, David J. Fleet:
Minimal Loss Hashing for Compact Binary Codes. 353-360 - Bo Chen, Gungor Polatkan, Guillermo Sapiro, David B. Dunson, Lawrence Carin:
The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning. 361-368 - Andrew Guillory, Jeff A. Bilmes:
Simultaneous Learning and Covering with Adversarial Noise. 369-376 - Haojun Chen, David B. Dunson, Lawrence Carin:
Topic Modeling with Nonparametric Markov Tree. 377-384 - Ankit Kuwadekar, Jennifer Neville:
Relational Active Learning for Joint Collective Classification Models. 385-392 - Abhishek Kumar, Hal Daumé III:
A Co-training Approach for Multi-view Spectral Clustering. 393-400 - Maayan Harel, Shie Mannor:
Learning from Multiple Outlooks. 401-408 - Michele Cossalter, Rong Yan, Lu Zheng:
Adaptive Kernel Approximation for Large-Scale Non-Linear SVM Prediction. 409-416 - Dario García-García, Ulrike von Luxburg, Raúl Santos-Rodríguez:
Risk-Based Generalizations of f-divergences. 417-424 - Novi Quadrianto, Christoph H. Lampert:
Learning Multi-View Neighborhood Preserving Projections. 425-432 - Francesco Orabona, Nicolò Cesa-Bianchi:
Better Algorithms for Selective Sampling. 433-440 - Sylvain Robbiano, Stéphan Clémençon:
Minimax Learning Rates for Bipartite Ranking and Plug-in Rules. 441-448 - Nikolay Jetchev, Marc Toussaint:
Task Space Retrieval Using Inverse Feedback Control. 449-456 - Seppo Virtanen, Arto Klami, Samuel Kaski:
Bayesian CCA via Group Sparsity. 457-464 - Marc Peter Deisenroth, Carl Edward Rasmussen:
PILCO: A Model-Based and Data-Efficient Approach to Policy Search. 465-472 - Masayuki Karasuyama, Ichiro Takeuchi:
Suboptimal Solution Path Algorithm for Support Vector Machine. 473-480 - Yi Sun, Faustino J. Gomez, Mark B. Ring, Jürgen Schmidhuber:
Incremental Basis Construction from Temporal Difference Error. 481-488 - Sean Gerrish, David M. Blei:
Predicting Legislative Roll Calls from Text. 489-496 - Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan:
On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution. 497-504 - Tom Bylander:
Learning Linear Functions with Quadratic and Linear Multiplicative Updates. 505-512 - Xavier Glorot
, Antoine Bordes, Yoshua Bengio:
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach. 513-520 - Zhuoliang Kang, Kristen Grauman, Fei Sha:
Learning with Whom to Share in Multi-task Feature Learning. 521-528 - Lev Reyzin:
Boosting on a Budget: Sampling for Feature-Efficient Prediction. 529-536 - Elena Ikonomovska, João Gama, Bernard Zenko, Saso Dzeroski:
Speeding-Up Hoeffding-Based Regression Trees With Options. 537-544 - Gilles Meyer, Silvère Bonnabel, Rodolphe Sepulchre:
Linear Regression under Fixed-Rank Constraints: A Riemannian Approach. 545-552 - Dijun Luo, Chris H. Q. Ding, Feiping Nie, Heng Huang:
Cauchy Graph Embedding. 553-560 - Manuel Gomez-Rodriguez, David Balduzzi, Bernhard Schölkopf:
Uncovering the Temporal Dynamics of Diffusion Networks. 561-568 - Tianshi Gao, Daphne Koller:
Multiclass Boosting with Hinge Loss based on Output Coding. 569-576 - Stefanie Jegelka, Jeff A. Bilmes:
Approximation Bounds for Inference using Cooperative Cuts. 577-584 - José Hernández-Orallo, Peter A. Flach, Cèsar Ferri Ramirez:
Brier Curves: a New Cost-Based Visualisation of Classifier Performance. 585-592 - Céline Brouard, Florence d'Alché-Buc, Marie Szafranski:
Semi-supervised Penalized Output Kernel Regression for Link Prediction. 593-600 - Sergey I. Nikolenko, Alexander Sirotkin:
A New Bayesian Rating System for Team Competitions. 601-608 - Arvind K. Sujeeth, HyoukJoong Lee, Kevin J. Brown, Tiark Rompf, Hassan Chafi, Michael Wu, Anand R. Atreya, Martin Odersky, Kunle Olukotun:
OptiML: An Implicitly Parallel Domain-Specific Language for Machine Learning. 609-616 - Jun Zhu, Ning Chen, Eric P. Xing:
Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines. 617-624 - Lingbo Li, Mingyuan Zhou, Guillermo Sapiro, Lawrence Carin:
On the Integration of Topic Modeling and Dictionary Learning. 625-632 - Benjamin M. Marlin, Mohammad Emtiyaz Khan, Kevin P. Murphy:
Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models. 633-640 - Ruth Urner, Shai Shalev-Shwartz, Shai Ben-David:
Access to Unlabeled Data can Speed up Prediction Time. 641-648 - Jean-Francis Roy, François Laviolette, Mario Marchand:
From PAC-Bayes Bounds to Quadratic Programs for Majority Votes. 649-656 - Peter A. Flach, José Hernández-Orallo, Cèsar Ferri Ramirez:
A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance. 657-664 - Vojtech Franc, Alexander Zien, Bernhard Schölkopf:
Support Vector Machines as Probabilistic Models. 665-672 - Omer Tamuz, Ce Liu, Serge J. Belongie, Ohad Shamir, Adam Kalai:
Adaptively Learning the Crowd Kernel. 673-680 - Max Welling, Yee Whye Teh:
Bayesian Learning via Stochastic Gradient Langevin Dynamics. 681-688 - Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, Andrew Y. Ng:
Multimodal Deep Learning. 689-696 - JooSeuk Kim, Clayton D. Scott:
On the Robustness of Kernel Density M-Estimators. 697-704 - Piyush Rai, Hal Daumé III:
Beam Search based MAP Estimates for the Indian Buffet Process. 705-712 - Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao:
Optimal Distributed Online Prediction. 713-720 - David A. Knowles, Jurgen Van Gael, Zoubin Ghahramani:
Message Passing Algorithms for the Dirichlet Diffusion Tree. 721-728 - Jian Peng, Tamir Hazan, David A. McAllester, Raquel Urtasun:
Convex Max-Product over Compact Sets for Protein Folding. 729-736 - Doran Chakraborty, Peter Stone:
Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function's In-Degree. 737-744 - Toby Hocking, Jean-Philippe Vert, Francis R. Bach, Armand Joulin:
Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties. 745-752 - Albert Shieh, Tatsunori B. Hashimoto, Edoardo M. Airoldi:
Tree preserving embedding. 753-760 - Raman Arora, Maya R. Gupta, Amol Kapila, Maryam Fazel:
Clustering by Left-Stochastic Matrix Factorization. 761-768 - Miao Liu, Xuejun Liao, Lawrence Carin:
The Infinite Regionalized Policy Representation. 769-776 - Michael L. Wick, Khashayar Rohanimanesh, Kedar Bellare, Aron Culotta, Andrew McCallum:
SampleRank: Training Factor Graphs with Atomic Gradients. 777-784 - XianXing Zhang, David B. Dunson, Lawrence Carin:
Tree-Structured Infinite Sparse Factor Model. 785-792 - Andrea Vattani, Deepayan Chakrabarti, Maxim Gurevich:
Preserving Personalized Pagerank in Subgraphs. 793-800 - Lin Xiao, Dengyong Zhou, Mingrui Wu:
Hierarchical Classification via Orthogonal Transfer. 801-808 - Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel:
A Three-Way Model for Collective Learning on Multi-Relational Data. 809-816 - Gerhard Neumann:
Variational Inference for Policy Search in changing situations. 817-824 - David Buffoni, Clément Calauzènes, Patrick Gallinari, Nicolas Usunier:
Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision. 825-832 - Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, Yoshua Bengio:
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction. 833-840 - Miguel Lázaro-Gredilla, Michalis K. Titsias:
Variational Heteroscedastic Gaussian Process Regression. 841-848 - Qiang Liu, Alexander Ihler:
Bounding the Partition Function using Holder's Inequality. 849-856 - Duy Quang Vu, Arthur U. Asuncion, David R. Hunter, Padhraic Smyth:
Dynamic Egocentric Models for Citation Networks. 857-864 - Kevin Small, Byron C. Wallace, Carla E. Brodley, Thomas A. Trikalinos:
The Constrained Weight Space SVM: Learning with Ranked Features. 865-872 - Yudong Chen, Huan Xu, Constantine Caramanis, Sujay Sanghavi:
Robust Matrix Completion and Corrupted Columns. 873-880 - Alborz Geramifard, Finale Doshi, Josh Redding, Nicholas Roy, Jonathan P. How:
Online Discovery of Feature Dependencies. 881-888 - John W. Paisley, Lawrence Carin, David M. Blei:
Variational Inference for Stick-Breaking Beta Process Priors. 889-896 - Monica Babes, Vukosi Marivate, Kaushik Subramanian, Michael L. Littman:
Apprenticeship Learning About Multiple Intentions. 897-904 - Jascha Sohl-Dickstein, Peter Battaglino, Michael Robert DeWeese:
Minimum Probability Flow Learning. 905-912 - Finale Doshi, David Wingate, Joshua B. Tenenbaum, Nicholas Roy:
Infinite Dynamic Bayesian Networks. 913-920 - Adam Coates, Andrew Y. Ng:
The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization. 921-928 - Marco Cuturi:
Fast Global Alignment Kernels. 929-936 - Loris Bazzani, Nando de Freitas, Hugo Larochelle, Vittorio Murino, Jo-Anne Ting:
Learning attentional policies for tracking and recognition in video with deep networks. 937-944 - Yann N. Dauphin, Xavier Glorot, Yoshua Bengio:
Large-Scale Learning of Embeddings with Reconstruction Sampling. 945-952 - Minmin Chen, Kilian Q. Weinberger, Yixin Chen:
Automatic Feature Decomposition for Single View Co-training. 953-960 - Kilho Shin, Marco Cuturi, Tetsuji Kuboyama:
Mapping kernels for trees. 961-968 - Pierre Machart, Thomas Peel, Sandrine Anthoine, Liva Ralaivola, Hervé Glotin:
Stochastic Low-Rank Kernel Learning for Regression. 969-976 - Kiyohito Nagano, Yoshinobu Kawahara, Kazuyuki Aihara:
Size-constrained Submodular Minimization through Minimum Norm Base. 977-984 - Lubor Ladicky, Philip H. S. Torr:
Locally Linear Support Vector Machines. 985-992 - Hachem Kadri, Asma Rabaoui, Philippe Preux, Emmanuel Duflos, Alain Rakotomamonjy:
Functional Regularized Least Squares Classication with Operator-valued Kernels. 993-1000 - Ali Jalali, Yudong Chen, Sujay Sanghavi, Huan Xu:
Clustering Partially Observed Graphs via Convex Optimization. 1001-1008 - Eunho Yang, Pradeep Ravikumar:
On the Use of Variational Inference for Learning Discrete Graphical Model. 1009-1016 - Ilya Sutskever, James Martens, Geoffrey E. Hinton:
Generating Text with Recurrent Neural Networks. 1017-1024 - Amrudin Agovic, Arindam Banerjee, Snigdhansu Chatterjee:
Probabilistic Matrix Addition. 1025-1032 - James Martens, Ilya Sutskever:
Learning Recurrent Neural Networks with Hessian-Free Optimization. 1033-1040 - Jacob Eisenstein, Amr Ahmed, Eric P. Xing:
Sparse Additive Generative Models of Text. 1041-1048 - Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Bruno Scherrer:
Classification-based Policy Iteration with a Critic. 1049-1056 - Abhimanyu Das, David Kempe:
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection. 1057-1064 - Ankur P. Parikh, Le Song, Eric P. Xing:
A Spectral Algorithm for Latent Tree Graphical Models. 1065-1072 - Yue Guan, Jennifer G. Dy, Michael I. Jordan:
A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection. 1073-1080 - Yufeng Li, Zhi-Hua Zhou:
Towards Making Unlabeled Data Never Hurt. 1081-1088 - Andrew M. Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, Andrew Y. Ng:
On Random Weights and Unsupervised Feature Learning. 1089-1096 - Miroslav Dudík, John Langford, Lihong Li:
Doubly Robust Policy Evaluation and Learning. 1097-1104 - Jiquan Ngiam, Zhenghao Chen, Pang Wei Koh, Andrew Y. Ng:
Learning Deep Energy Models. 1105-1112 - Wojciech Kotlowski, Krzysztof Dembczynski, Eyke Hüllermeier:
Bipartite Ranking through Minimization of Univariate Loss. 1113-1120 - Sangkyun Lee, Stephen J. Wright:
Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online Learning. 1121-1128 - Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions. 1129-1136 - Daniel Vainsencher, Ofer Dekel, Shie Mannor:
Bundle Selling by Online Estimation of Valuation Functions. 1137-1144 - Aaron C. Courville, James Bergstra, Yoshua Bengio:
Unsupervised Models of Images by Spikeand-Slab RBMs. 1145-1152 - Hetunandan Kamisetty, Eric P. Xing, Christopher James Langmead:
Approximating Correlated Equilibria using Relaxations on the Marginal Polytope. 1153-1160 - Yan Yan, Rómer Rosales, Glenn Fung, Jennifer G. Dy:
Active Learning from Crowds. 1161-1168 - Kevin Waugh, Brian D. Ziebart, Drew Bagnell:
Computational Rationalization: The Inverse Equilibrium Problem. 1169-1176 - Mohammad Ghavamzadeh, Alessandro Lazaric, Rémi Munos, Matthew W. Hoffman:
Finite-Sample Analysis of Lasso-TD. 1177-1184 - Jason Pazis, Ronald Parr:
Generalized Value Functions for Large Action Sets. 1185-1192 - Alex Kulesza, Ben Taskar:
k-DPPs: Fixed-Size Determinantal Point Processes. 1193-1200 - Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin, Nando de Freitas:
On Autoencoders and Score Matching for Energy Based Models. 1201-1208 - Alexander Grubb, Drew Bagnell:
Generalized Boosting Algorithms for Convex Optimization. 1209-1216
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