ICML 2010 – Accepted Papers
四月 24th, 2010
南大Zhi-Hua Zhou一篇，交大Yong Yu一篇。
The following 152 papers have been accepted:
- 16: Large Graph Construction for Scalable Semi-supervised Learning
Wei Liu, Junfeng He, Shih-Fu Chang - 23: Boosting Classifiers with Tightened L0-Relaxation Penalties
Noam Goldberg, Jonathan Eckstein - 26: Variable Selection in Model-Based Clustering: To Do or To Facilitate
Leonard Poon, Nevin Zhang, Tao Chen, Yi Wang - 28: Modeling Interaction via the Principle of Maximum Causal Entropy
Brian Ziebart, Drew Bagnell, Anind Dey - 35: Multi-Task Learning of Gaussian Graphical Models
Jean Honorio, Luis Ortiz, Dimitris Samaras - 45: Spherical Topic Models
Joseph Reisinger, Austin Waters, Bryan Silverthorn, Raymond Mooney - 52: Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Gavin Taylor, Marek Petrik, Ron Parr, Shlomo Zilberstein - 76: Multi-agent Learning Experiments on Repeated Matrix Games
Bruno Bouzy, Marc Métivier - 77: Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
Tobias Lang, Marc Toussaint
- 78: Causal filter selection in microarray data
Gianluca Bontempi, Patrick Meyer - 87: A Conditional Random Field for Multi-Instance Learning
Thomas Deselaers, Vittorio Ferrari - 99: Supervised Aggregation of Classifiers using Artificial Prediction Markets
Nathan Lay, Adrian Barbu - 100: 3D Convolutional Neural Networks for Human Action Recognition
Shuiwang Ji, Wei Xu, Ming Yang, Kai Yu - 107: Asymptotic Analysis of Generative Semi-Supervised Learning
Joshua Dillon, Krishnakumar Balasubramanian, Guy Lebanon - 115: Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate
Phil Long, Rocco Servedio - 117: Learning from Noisy Side Information by Generalized Maximum Entropy Model
Tianbao Yang, Rong Jin - 119: Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering
Nader Bshouty, Phil Long - 123: The Elastic Embedding Algorithm for Dimensionality Reduction
Miguel Carreira-Perpinan, Jianwu Zeng - 125: Two-Stage Learning Kernel Algorithms
Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh - 132: Robust Graph Mode Seeking by Graph Shift
Hairong Liu, Shuicheng Yan - 137: Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised Learning
Matan Gavish, Boaz Nadler, Ronald Coifman - 149: Deep Supervised T-Distributed Embedding
Renqiang Min, Zineng Yuan, Laurens van der Maaten, Anthony Bonner, Zhaolei Zhang - 168: A Nonparametric Information Theoretic Clustering Algorithm
Lev Faivishevsky, Jacob Goldberger - 170: Gaussian Process Change Point Models
Yunus Saatci, Ryan Turner, Carl Rasmussen - 175: Dynamical Products of Experts for Modeling Financial Time Series
Yutian Chen, Max Welling - 176: The Margin Perceptron with Unlearning
Constantinos Panagiotakopoulos, Petroula Tsampouka - 178: Sequential Projection Learning for Hashing with Compact Codes
Jun Wang, Sanjiv Kumar, Shih-Fu Chang
- 179: Generalization Bounds for Learning Kernels
Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh - 180: Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process
Kevin Canini, Tom Griffiths - 187: Convergence of Least Squares Temporal Difference Methods Under General Conditions
Huizhen Yu - 191: Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration
Michael Wunder, Michael Littman, Monica Babes - 195: Estimation of (near) low-rank matrices with noise and high-dimensional scaling
Sahand Negahban, Martin Wainwright - 196: A Simple Algorithm for Nuclear Norm Regularized Problems
Martin Jaggi, Marek Sulovský - 197: On Sparse Nonparametric Conditional Covariance Selection
Mladen Kolar, Ankur Parikh, Eric Xing - 202: Exploiting Data-Independence for Fast Belief-Propagation
Julian McAuley, Tiberio Caetano - 207: One-sided Support Vector Regression for Multiclass Cost-sensitive Classification
Han-Hsing Tu, Hsuan-Tien Lin - 219: OTL: A Framework of Online Transfer Learning
Peilin Zhao, Steven C.H. Hoi - 223: SVM Classifier Estimation from Group Probabilities
Stefan Rueping - 227: Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
Mingkui Tan, Li Wang, Ivor Tsang - 233: Total Variation and Cheeger Cuts
Arthur Szlam, Xavier Bresson - 235: Learning Temporal Graphs for Relational Time-Series Analysis
Yan Liu, Alexandru Niculescu-Mizil, Aurelie Lozano, Yong Lu - 238: Online Streaming Feature Selection
Kui Yu, Xindong Wu, Hao Wang - 242: Making Large-Scale Nystrom Approximation Possible
Mu Li, James Kwok, Bao-Liang Lu - 246: Particle Filtered MCMC-MLE with Connections to Contrastive Divergence
Arthur Asuncion, Qiang Liu, Alex Ihler, Padhraic Smyth - 247: Feature Selection as a one-player game
Romaric Gaudel, Michele Sebag - 248: The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data
Volker Roth, Thomas Fuchs, Julia Vogt, Sandhya Prabhakaran - 259: Online Prediction with Privacy
Jun Sakuma - 263: Fast boosting using adversarial bandits
Róbert Busa-Fekete, Balazs Kegl - 268: Robust Formulations for Handling Uncertainty in Kernel Matrices
Sahely Bhadra, Sourangshu Bhattacharya, Chiranjib Bhattacharyya, Aharon Ben-Tal - 269: Bayesian Multi-Task Reinforcement Learning
Mohammad Ghavamzadeh, Alessandro Lazaric - 275: A New Analysis of Co-Training
Wei Wang, Zhi-Hua Zhou - 279: Clustering processes
Daniil Ryabko - 280: COFFIN : A Computational Framework for Linear SVMs
Soeren Sonnenburg, Vojtech Franc - 284: Multiagent Inductive Learning: an Argumentation-based Approach
Santiago Ontanon, Enric Plaza - 285: Active Risk Estimation
Christoph Sawade, Niels Landwehr, Steffen Bickel, Tobias Scheffer - 286: Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing
Frank Dondelinger, Sophie Lebre, Dirk Husmeier - 295: Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda
Carlton Downey, Scott Sanner - 297: Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm
Rémi Bardenet, Balazs Kegl - 298: Random Spanning Trees and the Prediction of Weighted Graphs
Nicolo Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella - 303: Analysis of a Classification-based Policy Iteration Algorithm
Mohammad Ghavamzadeh, Alessandro Lazaric, Remi Munos - 310: Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes
Zeeshan Syed, Ilan Rubinfeld - 311: Gaussian Covariance and Scalable Variational Inference
Matthias Seeger - 319: Efficient Learning with Partially Observed Attributes
Ohad Shamir, Nicolo Cesa-Bianchi, Shai Shalev-Shwartz - 330: Boosting for Regression Transfer
David Pardoe, Peter Stone - 331: Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences
Antoine Bordes, Nicolas Usunier, Jason Weston - 333: From Transformation-Based Dimensionality Reduction to Feature Selection
Mahdokht Masaeli, Glenn Fung, Jennifer Dy - 336: Least-Squares λ Policy Iteration: Bias-Variance Trade-off in Control Problems
Christophe Thiery, Bruno Scherrer - 342: Multiple Non-Redundant Spectral Clustering Views
Donglin Niu, Jennifer Dy - 344: Large Scale Max-Margin Multi-Label Classification with Prior Knowledge about Densely Correlated Labels
Bharath Hariharan, S.V.N. Vishwanathan, Manik Varma - 347: Fast Neighborhood Subgraph Pairwise Distance Kernel
Fabrizio Costa, Kurt De Grave - 352: Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity
Seyoung Kim, Eric Xing - 353: Label Ranking Methods based on the Plackett-Luce Model
Weiwei Cheng, Krzysztof Dembczynski, Eyke Huellermeier - 359: A DC Programming Approach for Sparse Eigenvalue Problem
Mamadou Thiao, Tao Pham Dinh, Hoai An Le Thi - 366: Dictionary Selection for Sparse Representation
Andreas Krause, Volkan Cevher - 370: Deep networks for robust visual recognition
Yichuan Tang, Chris Eliasmith - 371: A Stick-Breaking Construction of the Beta Process
John Paisley, Lawrence Carin - 374: Local Minima Embedding
Minyoung Kim, Fernando De la Torre - 376: Risk minimization, probability elicitation, and cost-sensitive SVMs
Hamed Masnadi-Shirazi, Nuno Vasconcelos - 378: Continuous-Time Belief Propagation
Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman - 384: Measuring Article Influence Without Citations
Sean Gerrish, David Blei - 387: Power Iteration Clustering
Frank Lin, William Cohen - 397: The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling
Sinead Williamson, Chong Wang, Katherine Heller, David Blei - 406: Budgeted Distribution Learning of Belief Net Parameters
Barnabas Poczos, Russell Greiner, Csaba Szepesvari, Liuyang Li - 410: Efficient Selection of Multiple Bandit Arms: Theory and Practice
Shivaram Kalyanakrishnan, Peter Stone - 412: Gaussian Process Multiple Instance Learning
Minyoung Kim, Fernando De la Torre - 416: Proximal Methods for Sparse Hierarchical Dictionary Learning
Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis Bach - 420: Conditional Topic Random Fields
Jun Zhu, Eric Xing - 421: On the Consistency of Ranking Algorithms
John Duchi, Lester Mackey, Michael Jordan - 422: Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
Niranjan Srinivas, Andreas Krause, Sham Kakade, Matthias Seeger - 429: Implicit Online Learning
Brian Kulis, Peter Bartlett - 432: Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair, Geoffrey Hinton - 433: Budgeted Learning from Data Streams
Ryan Gomes, Andreas Krause - 436: Interactive Submodular Set Cover
Andrew Guillory, Jeff Bilmes - 438: A fast natural Newton method
Nicolas Le Roux, Andrew Fitzgibbon - 441: Learning Deep Boltzmann Machines using Adaptive MCMC
Ruslan Salakhutdinov - 442: Internal Rewards Mitigate Agent Boundedness
Jonathan Sorg, Satinder Singh, Richard Lewis - 446: Learning optimally diverse rankings over large document collections
Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi - 449: Learning Fast Approximations of Sparse Coding
Karol Gregor, Yann LeCun - 451: Boosted Backpropagation Learning for Training Deep Modular Networks
Alexander Grubb, Drew Bagnell - 453: Convergence, Targeted Optimality, and Safety in Multiagent Learning
Doran Chakraborty, Peter Stone - 454: Improved Local Coordinate Coding using Local Tangents
Kai Yu, Tong Zhang - 458: Deep learning via Hessian-free optimization
James Martens - 464: Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis
Daniel Lizotte, Michael Bowling, Susan Murphy - 468: Cognitive Models of Test-Item Effects in Human Category Learning
Xiaojin Zhu, Bryan Gibson, Kwang-Sung Jun, Tim Rogers - 473: Online Learning for Group Lasso
Haiqin Yang, Zenglin Xu, Irwin King, Michael Lyu - 475: Generalizing Apprenticeship Learning across Hypothesis Classes
Thomas Walsh, Kaushik Subramanian, Michael Littman, Carlos Diuk - 481: Projection Penalties: Dimension Reduction without Loss
Yi Zhang, Jeff Schneider - 493: Application of Machine Learning To Epileptic Seizure Detection
Ali Shoeb - 495: Hilbert Space Embeddings of Hidden Markov Models
Le Song, Byron Boots, Sajid Saddiqi, Geoffrey Gordon, Alex Smola - 502: Learning Markov Logic Networks Using Structural Motifs
Stanley Kok, Pedro Domingos - 504: Metric Learning to Rank
Brian McFee, Gert Lanckriet - 505: Collective Link Prediction in Multiple Heterogenous Domains
Bin Cao, Nathan Liu, Qiang Yang - 518: On Non-identifiability of Bayesian Matrix Factorization Models
Shinichi Nakajima, Masashi Sugiyama - 520: On learning with kernels for unordered pairs
Martial Hue, Jean-Philippe Vert - 521: Robust Subspace Segmentation by Low-Rank Representation
Guangcan Liu, Zhouchen Lin, Yong Yu - 522: Structured Output Learning with Indirect Supervision
Ming-Wei Chang, Vivek Srikumar, Dan Goldwasser, Dan Roth - 523: Bayesian Nonparametric Matrix Factorization for Recorded Music
Matthew Hoffman, David Blei, Perry Cook - 532: Learning the Linear Dynamical System with ASOS
James Martens - 537: Bottom-Up Learning of Markov Network Structure
Jesse Davis, Pedro Domingos - 540: Simple and Efficient Multiple Kernel Learning By Group Lasso
Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, Michael Lyu - 544: Active Learning for Networked Data
Mustafa Bilgic, Lilyana Mihalkova, Lise Getoor - 546: Model-based reinforcement learning with nearly tight exploration complexity bounds
Istvan Szita, Csaba Szepesvari - 549: Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process
Nicholas Bartlett, David Pfau, Frank Wood - 551: Distance Dependent Chinese Restaurant Processes
David Blei, Peter Frazier - 553: Mixed Membership Matrix Factorization
Lester Mackey, David Weiss, Michael Jordan - 554: An Analysis of the Convergence of Graph Laplacians
Daniel Ting - 556: An Efficient and General Augmented Lagrangian Algorithm for Learning Low-Rank Matrices
Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima - 562: A scalable trust-region algorithm with application to mixed-norm regression
Dongmin Kim, Suvrit Sra, Inderjit Dhillon - 568: Learning Programs: A Hierarchical Bayesian Approach
Percy Liang, Michael Jordan, Dan Klein - 569: Multi-Class Pegasos on a Budget
Zhuang Wang, Koby Crammer, Slobodan Vucetic - 571: Inverse Optimal Control with Linearly Solvable MDPs
Krishnamurthy Dvijotham, Emanuel Todorov - 576: Telling cause from effect based on high-dimensional observations
Dominik Janzing, Patrik Hoyer, Bernhard Schoelkopf - 582: Mining Clustering Dimensions
Sajib Dasgupta, Vincent Ng - 586: Learning Tree Conditional Random Fields
Joseph Bradley, Carlos Guestrin - 587: Learning efficiently with approximate inference via dual losses
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Globerson - 588: Approximate Predictive Representations of Partially Observable Systems
Doina Precup, Monica Dinculescu - 589: Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
Krzysztof Dembczynski, Weiwei Cheng, Eyke Huellermeier - 592: Non-Local Contrastive Objectives
David Vickrey, Cliff Lin, Daphne Koller - 593: Constructing States for Reinforcement Learning
M. M. Mahmud - 596: Graded Multilabel Classification: The Ordinal Case
Weiwei Cheng, Krzysztof Dembczynski, Eyke Huellermeier - 598: Finite-Sample Analysis of LSTD
Mohammad Ghavamzadeh, Alessandro Lazaric, Remi Munos - 601: On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning
Percy Liang, Nathan Srebro - 605: Learning Hierarchical Riffle Independent Groupings from Rankings
Jonathan Huang, Carlos Guestrin - 620: Active Learning for Multi-Task Adaptive Filtering
Abhay Harpale, Yiming Yang - 627: Toward Off-Policy Learning Control with Function Approximation
Hamid Maei, Csaba Szepesvari, Shalabh Bhatnagar, Richard Sutton - 628: Fast and smooth: Accelerated dual decomposition for MAP inference
Vladimir Jojic, Stephen Gould, Daphne Koller - 636: Sparse Gaussian Process Regression via L1 Penalization
Feng Yan, Yuan Qi - 638: A theoretical analysis of feature pooling in vision algorithms
Y-Lan Boureau, Jean Ponce, Yann LeCun - 642: Comparing Clusterings in Space
Michael Coen, Hidayath Ansari, Nathanael Fillmore - 643: Discriminative Semi-Supervised Learning by Encouraging Generative Models to Discover Relevant Latent Representations
Gregory Druck, Andrew McCallum - 652: Nonparametric Return Density Estimation Reinforcement Learning
Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka - 654: Should one compute the Temporal Difference fix point or minimize the Bellman Residual?
Bruno Scherrer
中国的还有合肥科技大学一篇