ICML 2010 - Accepted Papers

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

South University of Science and Technology of China

校网址：http://210.22.13.218/cn/

南科概况

创建中的南方科技大学地处中国改革开放的第一个经济特区——深圳。深圳是中国对外开放的窗口，是连接香港与中国内地的纽带和桥梁，是在改革开放三十 年中迅速崛起的现代化新兴城市。 南方科技大学是在中国高等教育改革发展的宏观背景下，深圳市人民政府落实《珠江三角洲地区改革发展规划纲要(2008-2020)》，以新的思维和机制筹 建的一所新大学。南方科技大学以理工学科为主，兼有管理学科及部分文科专业。学校将借鉴国内外大学的成功经验，以亚洲一流的标准组建每一个专业、系和学 院，及相应的研究室（所），建成小规模高质量的研究型大学。

新时期国家明确了深圳作为国家综合改革配套试验区、全国经济中心城市、国家创新型城市和国际化城市的重要定位。深圳将继续承担探索科学发展模式，深 化改革先行先试的重要使命。南方科技大学是深圳继改革开放30年在经济体制改革上取得重要贡献之后，为国家探索建设世界一流大学新路，尝试中国特色创新人 才培养新模式，并通过建设一流大学推动区域经济发展，实现中华民族复兴的创新实践。

南方科技大学的办学经费由深圳市政府财政拨付。同时拟启动成立南方科技大学基金会，广泛募集来自社会和民间的资金支持，逐步形成政府资金投入为主、 办学经费来源多样化的格局。

校徽介绍与校名解读

校徽的核心部分是一把火炬，象征南方科技大学的使命：为高等教育改革探索出一条新路。校徽背景为渐变的天青色， 映衬火炬的照亮效果；此处的天青背景取自汝窑瓷釉，是中国传统审美文化崇尚的色彩。

校名“南方科技大学”六个字的书法选自中国历史上褚遂良、柳公权、颜真卿等书法大家的作品，校名书写的独特组合 方式，代表着学校“博采众家之长”的精神。

终于解放了

休整了一年多，现在终于开通了