28. ICML 2011:
Bellevue, Washington, USA
Lise Getoor, Tobias Scheffer (Eds.):
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 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 L. 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 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 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, Silvere 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 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 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 Bach, Armand Joulin:
Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties.
745-752
- Albert Shieh, Tatsunori Hashimoto, Edo 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 T. 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 William Paisley, Lawrence Carin, David M. Blei:
Variational Inference for Stick-Breaking Beta Process Priors.
889-896
- Monica Babes, Vukosi N. Marivate, Kaushik Subramanian, Michael L. Littman:
Apprenticeship Learning About Multiple Intentions.
897-904
- Jascha Sohl-Dickstein, Peter Battaglino, Michael R. 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 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 D. 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
- Yu-Feng 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 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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