Home

Program

For Participants

For authors

Misc

ICML 2010 - Schedule

Download the full program to your own calendar: HTML ICAL XML

Mon 09.00 (Tamar)	Geometric Tools for Identifying Structure in Large Social and Information Networks Michael W. Mahoney
Mon 09.00 (Rimon)	Domain Adaptation Hal Daume & John Blitzer
Mon 09.00 (Alon)	Sparse Modeling: Theory, Algorithms and Applications Irina Rish & Genady Grabarnik

Mon 13.00 (Alon)	Learning through Exploration Alina Beygelzimer and John Langford
Mon 13.00 (Tamar)	Metric Learning Brian Kulis
Mon 13.00 (Rimon)	Privacy-preserving Data Mining Stan Matwin

Mon 16.00 (Rimon)	Sparse Modeling: Theory, Algorithms and Applications Irina Rish & Genady Grabarnik
Mon 16.00 (Tamar)	Domain Adaptation Hal Daume & John Blitzer
Mon 16.00 (Alon)	Stochastic Optimization for Machine Learning Nathan Srebro and Ambuj Tewari

Tue 08.30 (Oren)

ICML Opening

Tue 09.00 (Oren)

Invited Talk: Tom Mitchell

Tue 10.30 (Alon)	Topic Models and Matrix Factorization
45: Spherical Topic Models J. Reisinger, A. Waters, B. Silverthorn, R. Mooney 420: Conditional Topic Random Fields J. Zhu, E. Xing 384: A Language-based Approach to Measuring Scholarly Impact S. Gerrish, D. Blei 553: Mixed Membership Matrix Factorization L. Mackey, D. Weiss, M. Jordan

Tue 10.30 (Tamar)

Reinforcement Learning 1

336: Least-Squares lambda Policy Iteration: Bias-Variance Trade-off in Control Problems
C. Thiery, B. Scherrer
598: Finite-Sample Analysis of LSTD
A. Lazaric, M. Ghavamzadeh, R. Munos
187: Convergence of Least Squares Temporal Difference Methods Under General Conditions
H. Yu
654: Should one compute the Temporal Difference fix point or minimize the Bellman Residual? The unified oblique projection view
B. Scherrer

Tue 10.30 (Rimon)	Ensemble Methods
263: Fast boosting using adversarial bandits Róbert Busa-Fekete, Balazs Kegl 330: Boosting for Regression Transfer D. Pardoe, P. Stone 99: Supervised Aggregation of Classifiers using Artificial Prediction Markets N. Lay, A. Barbu 23: Boosting Classifiers with Tightened L0-Relaxation Penalties N. Goldberg, J. Eckstein

Tue 10.30 (Hadas)	Statistical Relational Learning
77: Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds T. Lang, M. Toussaint 544: Active Learning for Networked Data M. Bilgic, L. Mihalkova, L. Getoor 502: Learning Markov Logic Networks Using Structural Motifs S. Kok, P. Domingos 537: Bottom-Up Learning of Markov Network Structure J. Davis, P. Domingos

Tue 10.30 (Arava)	Large-Scale Learning and Optimization
438: A fast natural Newton method N. Le Roux, A. Fitzgibbon 562: A scalable trust-region algorithm with application to mixed-norm regression D. Kim, S. Sra, I. Dhillon 242: Making Large-Scale Nystrom Approximation Possible M. Li, J. Kwok, B.-L. Lu 311: Gaussian Covariance and Scalable Variational Inference M. Seeger

Tue 13.30 (Alon)

Matrix Factorization and Recommendation

518: Implicit Regularization in Variational Bayesian Matrix Factorization
S. Nakajima, M. Sugiyama
523: Bayesian Nonparametric Matrix Factorization for Recorded Music
M. Hoffman, D. Blei, P. Cook
196: A Simple Algorithm for Nuclear Norm Regularized Problems
M. Jaggi, M. Sulovský
505: Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains
B. Cao, N. Liu, Q. Yang

Tue 13.30 (Tamar)	Reinforcement Learning 2
588: Approximate Predictive Representations of Partially Observable Systems D. Precup, M. Dinculescu 593: Constructing States for Reinforcement Learning M. M. Mahmud 295: Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda C. Downey, S. Sanner 269: Bayesian Multi-Task Reinforcement Learning A. Lazaric, M. Ghavamzadeh

Tue 13.30 (Rimon)	Deep Learning 1
100: 3D Convolutional Neural Networks for Human Action Recognition S. Ji, W. Xu, M. Yang, K. Yu 370: Deep networks for robust visual recognition Y. Tang, C. Eliasmith 451: Boosted Backpropagation Learning for Training Deep Modular Networks A. Grubb, D. Bagnell 458: Deep learning via Hessian-free optimization J. Martens

Tue 13.30 (Hadas)	Multi-Task and Transfer Learning
620: Active Learning for Multi-Task Adaptive Filtering A. Harpale, Y. Yang 352: Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity S. Kim, E. Xing 219: OTL: A Framework of Online Transfer Learning P. Zhao, S. C.H. Hoi

Tue 13.30 (Oren)	Ranking and Preference Learning
331: Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences A. Bordes, N. Usunier, J. Weston 353: Label Ranking Methods based on the Plackett-Luce Model W. Cheng, K. Dembczynski, E. Hüllermeier 504: Metric Learning to Rank B. McFee, G. Lanckriet 605: Learning Hierarchical Riffle Independent Groupings from Rankings J. Huang, C. Guestrin

Tue 15.40 (Alon)

Latent-Variable Models

397: The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling
S. Williamson, C. Wang, K. Heller, D. Blei
549: Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process
N. Bartlett, D. Pfau, F. Wood
371: A Stick-Breaking Construction of the Beta Process
J. Paisley, A. Zaas, C. Woods, G. Ginsburg, L. Carin
551: Distance Dependent Chinese Restaurant Processes
D. Blei, P. Frazier

Tue 15.40 (Tamar)

Reinforcement Learning 3

475: Generalizing Apprenticeship Learning across Hypothesis Classes
T. Walsh, K. Subramanian, M. Littman, C. Diuk
627: Toward Off-Policy Learning Control with Function Approximation
H. Maei, C. Szepesvari, S. Bhatnagar, R. Sutton
464: Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis
D. Lizotte, M. Bowling, S. Murphy
442: Internal Rewards Mitigate Agent Boundedness
J. Sorg, S. Singh, R. Lewis

Tue 15.40 (Rimon)	Deep Learning 2
115: Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate P. Long, R. Servedio 149: Deep Supervised T-Distributed Embedding R. Min, L. van der Maaten, Z. Yuan, A. Bonner, Z. Zhang 432: Rectified Linear Units Improve Restricted Boltzmann Machines V. Nair, G. Hinton 441: Learning Deep Boltzmann Machines using Adaptive MCMC R. Salakhutdinov

Tue 15.40 (Hadas)	Structured Output Learning
522: Structured Output Learning with Indirect Supervision M.-W. Chang, V. Srikumar, D. Goldwasser, Dan Roth 586: Learning Tree Conditional Random Fields J. Bradley, C. Guestrin 587: Learning efficiently with approximate inference via dual losses O. Meshi, D. Sontag, T. Jaakkola, A. Globerson

Tue 15.40 (Oren)

Dimensionality Reduction 1

195: Estimation of (near) low-rank matrices with noise and high-dimensional scaling
S. Negahban, M. Wainwright
359: A DC Programming Approach for Sparse Eigenvalue Problem
Mamadou Thiao, Tao Pham Dinh, Hoai An Le Thi
556: A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices
R. Tomioka, T. Suzuki, M. Sugiyama, H. Kashima
197: On Sparse Nonparametric Conditional Covariance Selection
M. Kolar, A. Parikh, E. Xing

Tue 17.30 (Oren)

Best 10-Year Paper: Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
Erin L. Allwein, Robert E. Schapire, Yoram Singer

Tue 18.00
(Oren Foyer)

Poster Session 1

Wed 08.30 (Oren)

Best Paper: Hilbert Space Embeddings of Hidden Markov Models
L. Song, B. Boots, S. Siddiqi, G. Gordon, A. Smola

Wed 09.00 (Oren)

Invited Talk: Duncan Watts

Wed 10.30 (Alon)	Graph Clustering
119: Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering N. Bshouty, P. Long 233: Total Variation and Cheeger Cuts A. Szlam, X. Bresson 387: Power Iteration Clustering F. Lin, W. Cohen 132: Robust Graph Mode Seeking by Graph Shift H. Liu, S. Yan

Wed 10.30 (Tamar)

Reinforcement Learning 4

303: Analysis of a Classification-based Policy Iteration Algorithm
A. Lazaric, M. Ghavamzadeh, R. Munos
652: Nonparametric Return Distribution Approximation for Reinforcement Learning
T. Morimura, M. Sugiyama, H. Kashima, H. Hachiya, T. Tanaka
571: Inverse Optimal Control with Linearly Solvable MDPs
K. Dvijotham, E. Todorov
52: Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
M. Petrik, G. Taylor, R. Parr, S. Zilberstein

Wed 10.30 (Rimon)

Risk estimation and Cost-sensitive Learning

376: Risk minimization, probability elicitation, and cost-sensitive SVMs
Hamed Masnadi-Shirazi, n. Vasconcelos
207: One-sided Support Vector Regression for Multiclass Cost-sensitive Classification
H.-H. Tu, H.-T. Lin
285: Active Risk Estimation
C. Sawade, N. Landwehr, S. Bickel, T. Scheffer
310: Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes
Z. Syed, I. Rubinfeld

Wed 10.30 (Hadas)	Kernels
125: Two-Stage Learning Kernel Algorithms C. Cortes, M. Mohri, A. Rostamizadeh 347: Fast Neighborhood Subgraph Pairwise Distance Kernel F. Costa, K. De Grave 520: On learning with kernels for unordered pairs M. Hue, J.-P. Vert 179: Generalization Bounds for Learning Kernels C. Cortes, M. Mohri, A. Rostamizadeh

Wed 10.30 (Oren)	Dimensionality Reduction 2
374: Local Minima Embedding M. Kim, F. De la Torre 481: Projection Penalties: Dimension Reduction without Loss Y. Zhang, J. Schneider 123: The Elastic Embedding Algorithm for Dimensionality Reduction Miguel Carreira-Perpinan 333: From Transformation-Based Dimensionality Reduction to Feature Selection M. Masaeli, G. Fung, J. Dy

Wed 13.30 (Oren)

Guest Lecture: Robert Aumann

Wed 14.30 (Oren)

Best Application Paper:
Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process
K. Canini, M. Shashkov, T. Griffiths

Wed 15.40 (Alon)	Invited Applications 1
901: Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine T. Graepel, J. Quiñonero Candela, T. Borchert, R. Herbrich 902: Detecting Large-Scale System Problems by Mining Console Logs W. Xu, L. Huang, A. Fox, D. Patterson, M. I. Jordan 903: The Role of Machine Learning in Business Optimization C. Apte

Wed 15.40 (Tamar)	Clustering 1
248: The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data J. Vogt, S. Prabhakaran, T. Fuchs, V. Roth 279: Clustering processes D. Ryabko 26: Variable Selection in Model-Based Clustering: To Do or To Facilitate L. Poon, N. Zhang, T. Chen, Y. Wang 168: A Nonparametric Information Theoretic Clustering Algorithm L. Faivishevsky, J. Goldberger

Wed 15.40 (Rimon)

Causal Inference

235: Learning Temporal Graphs for Relational Time-Series Analysis
Y. Liu, Alexandru Niculescu-Mizil, A. Lozano, Y. Lu
78: Causal filter selection in microarray data
G. Bontempi, P. Meyer
576: Telling cause from effect based on high-dimensional observations
D. Janzing, P. Hoyer, B. Schölkopf
28: Modeling Interaction via the Principle of Maximum Causal Entropy
B. Ziebart, D. Bagnell, A. Dey

Wed 15.40 (Hadas)	Large Margin Methods
176: The Margin Perceptron with Unlearning C. Panagiotakopoulos, P. Tsampouka 569: Multi-Class Pegasos on a Budget Z. Wang, K. Crammer, S. Vucetic 280: COFFIN : A Computational Framework for Linear SVMs S. Sonnenburg, V. Franc 268: Robust Formulations for Handling Uncertainty in Kernel Matrices S. Bhadra, S. Bhattacharya, C. Bhattacharyya, Aharon Ben-Tal

Wed 15.40 (Oren)	Compact Representations
449: Learning Fast Approximations of Sparse Coding K. Gregor, Y. LeCun 366: Submodular Dictionary Selection for Sparse Representation A. Krause, V. Cevher 416: Proximal Methods for Sparse Hierarchical Dictionary Learning R. Jenatton, J. Mairal, G. Obozinski, F. Bach 178: Sequential Projection Learning for Hashing with Compact Codes J. Wang, S. Kumar, S.-F. Chang

Wed 19.00

Banquet

Thu 08.30 (Oren)

Best Student Paper: On the Consistency of Ranking Algorithms
J. Duchi, L. Mackey, M. Jordan

Thu 09.00 (Oren)

Invited Talk: Nir Friedman

Thu 10.30 (Alon)

Graphical Models

286: Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing
F. Dondelinger, S. Lebre, D. Husmeier
378: Continuous-Time Belief Propagation
T. El-Hay, I. Cohn, N. Friedman, R. Kupferman
202: Exploiting Data-Independence for Fast Belief-Propagation
J. McAuley, T. Caetano
628: Accelerated dual decomposition for MAP inference
V. Jojic, S. Gould, D. Koller

Thu 10.30 (Tamar)	Clustering 2
342: Multiple Non-Redundant Spectral Clustering Views D. Niu, J. Dy, M. Jordan 582: Mining Clustering Dimensions S. Dasgupta, V. Ng 642: Comparing Clusterings in Space M. Coen, H. Ansari, N. Fillmore 521: Robust Subspace Segmentation by Low-Rank Representation G. Liu, Z. Lin, Y. Yu

Thu 10.30 (Rimon)	Feature and Kernel Selection
540: Simple and Efficient Multiple Kernel Learning By Group Lasso Z. Xu, R. Jin, H. Yang, I. King, M. Lyu 238: Online Streaming Feature Selection X. Wu, K. Yu, H. Wang, W. Ding 247: Feature Selection as a One-Player Game R. Gaudel, M. Sebag 227: Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets M. Tan, L. Wang, I. Tsang

Thu 10.30 (Hadas)	Learning Theory
319: Efficient Learning with Partially Observed Attributes N. Cesa-Bianchi, S. Shalev-Shwartz, O. Shamir 601: On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning P. Liang, N. Srebro 554: An Analysis of the Convergence of Graph Laplacians D. Ting, L. Huang, M. Jordan

Thu 10.30 (Oren)

Exploration and Feature Construction

410: Efficient Selection of Multiple Bandit Arms: Theory and Practice
S. Kalyanakrishnan, P. Stone
546: Model-based reinforcement learning with nearly tight exploration complexity bounds
I. Szita, C. Szepesvari
638: A theoretical analysis of feature pooling in vision algorithms
Y.-L. Boureau, J. Ponce, Y. LeCun
454: Improved Local Coordinate Coding using Local Tangents
K. Yu, T. Zhang

Thu 13.30 (Alon)

Invited Applications 2

904: Music Plus One and Machine Learning
C. Raphael
905: Climbing the Tower of Babel: Unsupervised Multilingual Learning
B. Snyder, R. Barzilay
906: FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model
M. Cummins, P. Newman
907: Discriminative Latent Variable Models for Object Detection
P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan

Thu 13.30 (Tamar)

Semi-Supervised Learning 1

16: Large Graph Construction for Scalable Semi-Supervised Learning
W. Liu, J. He, S.-F. Chang
137: Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised Learning
M. Gavish, B. Nadler, R. Coifman
643: High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models
G. Druck, A. McCallum
107: Asymptotic Analysis of Generative Semi-Supervised Learning
J. Dillon, K. Balasubramanian, G. Lebanon

Thu 13.30 (Rimon)

Gaussian Processes

636: Sparse Gaussian Process Regression via L_1 Penalization
F. Yan, Y. Qi
297: Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm
R. Bardenet, B. Kegl
412: Gaussian Processes Multiple Instance Learning
M. Kim, F. De la Torre
422: Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
N. Srinivas, A. Krause, S. Kakade, M. Seeger

Thu 13.30 (Hadas)	Online Learning
259: Online Prediction with Privacy J. Sakuma, H. Arai 429: Implicit Online Learning B. Kulis, P. Bartlett 473: Online Learning for Group Lasso H. Yang, Z. Xu, I. King, M. Lyu 298: Random Spanning Trees and the Prediction of Weighted Graphs N. Cesa-Bianchi, C. Gentile, F. Vitale, G. Zappella

Thu 13.30 (Oren)

Multi-Agent Learning

76: Multi-agent Learning Experiments on Repeated Matrix Games
B. Bouzy, M. Métivier
191: Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration
M. Wunder, M. Littman, M. Babes
284: Multiagent Inductive Learning: an Argumentation-based Approach
Santiago Ontanon, E. Plaza
453: Convergence, Targeted Optimality, and Safety in Multiagent Learning
D. Chakraborty, P. Stone

Thu 15.40 (Alon)	Graphical Models and Bayesian Methods
246: Particle Filtered MCMC-MLE with Connections to Contrastive Divergence A. Asuncion, Q. Liu, A. Ihler, P. Smyth 592: Non-Local Contrastive Objectives D. Vickrey, C. Chiung-Yu Lin, D. Koller 35: Multi-Task Learning of Gaussian Graphical Models J. Honorio, D. Samaras 568: Learning Programs: A Hierarchical Bayesian Approach P. Liang, M. Jordan, D. Klein

Thu 15.40 (Tamar)	Semi-Supervised Learning 2
468: Cognitive Models of Test-Item Effects in Human Category Learning X. Zhu, B. Gibson, K.-S. Jun, T. Rogers, J. Harrison, Chuck Kalish 275: A New Analysis of Co-Training W. Wang, Z.-H. Zhou 117: Learning from Noisy Side Information by Generalized Maximum Entropy Model T. Yang, R. Jin, A. Jain 223: SVM Classifier Estimation from Group Probabilities S. Rueping

Thu 15.40 (Rimon)	Time-Series Analysis
493: Application of Machine Learning To Epileptic Seizure Detection A. Shoeb, J. Guttag 175: Dynamical Products of Experts for Modeling Financial Time Series Y. Chen, Max Welling 170: Gaussian Process Change Point Models Y. Saatci, R. Turner, C. Rasmussen 532: Learning the Linear Dynamical System with ASOS J. Martens

Thu 15.40 (Hadas)	Online and Active Learning
406: Budgeted Distribution Learning of Belief Net Parameters B. Poczos, L. Li, C. Szepesvari, R. Greiner 436: Interactive Submodular Set Cover A. Guillory, J. Bilmes 446: Learning optimally diverse rankings over large document collections A. Slivkins, F. Radlinski, S. Gollapudi 433: Budgeted Nonparametric Learning from Data Streams R. Gomes, A. Krause

Thu 15.40 (Oren)

Multi-Label and Multi-Instance Learning

87: A Conditional Random Field for Multi-Instance Learning
T. Deselaers, V. Ferrari
344: Large Scale Max-Margin Multi-Label Classification with Priors
B. Hariharan, Lihi Zelnik-Manor, S.V.N. Vishwanathan, M. Varma
589: Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
K. Dembczynskì, W. Cheng, E. Hüllermeier
596: Graded Multilabel Classification: The Ordinal Case
W. Cheng, K. Dembczynski, E. Hüllermeier

Thu 17.30 (Oren)

Business Meeting

Thu 18.30
(Oren Foyer)

Poster Session 2

Fri 08.30 (King Shlomo, Dan Carmel)

Budgeted Learning
Dragos Margineantu, Russell Greiner, Tomas Singliar and Prem Melville

Fri 09.00 (Alon, Dan Panorama)	Reinforcement Learning and Search in Very Large Spaces Peter Auer, Samuel Kaski and Csaba Szepesvari
Fri 09.00 (Rimon, Dan Carmel)	Social Analytics: Learning from Human Interactions Elad Yom-Tov, Shie Mannor and Yossi Richter
Fri 09.00 (Erez, Dan Panorama)	Machine Learning and Open Source Software Soeren Sonnenburg, Mikio Braun, Cheng Soon Ong and Patrik Hoyer
Fri 09.00 (Dekel, Dan Carmel)	Learning to Rank Challenge Tie-Yan Liu, Olivier Chapelle and Yi Chang
Fri 09.00 (Oren, Dan Panorama)	Topic Models; Structure, Applications, Evaluation, and Extensions Michal Rosen-Zvi, Amit Gruber and Richard Zemel
Fri 09.00 (King David, Dan Carmel)	Learning from Multi-Label Data Min-Ling Zhang, Grigorios Tsoumakas and Zhi-Hua Zhou
Fri 09.00 (Brosh, Dan Panorama)	Machine Learning and Games Kurt Driessens, Olana Missura and Christian Thurau
Fri 09.00 (Tomer, Dan Panorama)	Learning in Non-(geo)metric Spaces Joachim Buhmann, Robert Duin, Mario Figueiredo, Edwin Hancock, Vittorio Murino and Marcello Pelillo