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Conference schedule


Date Events
Sun 6/26 Outdoor events
Mon 6/27 ICML/ACL/ISCA Symposium
Tue 6/28 Tutorials
Wed 6/29
Main Session
Poster Session 1
Thu 6/30
Main Session
Fri 7/1
Main Session
Poster Session 2
Sat 7/2 Workshops

Detailed main session schedule

ICML 2011 has 5 parallel tracks denoted by letters. The letter in the session identifier indicates the track. The tracks map to sections of the ballroom as follows:

TrackBallroom sections

Wed, 29 June

TimeSessionSession Title
Session Chair
Paper TitleAuthors
Wed, 8.30-9 1AWelcomeWelcome address and Best Paper AwardsZoubin Ghahramani, Lise Getoor, Tobias Scheffer
Wed, 9-10 1AKeynote

John Platt
Embracing Uncertainty: Applied Machine Learning Comes of AgeChristopher Bishop
Wed, 10-10.30 Coffee Break
Wed, 10.30-12.10 2ABandits and Online Learning

John Langford
Unimodal BanditsJia Yuan Yu; Shie Mannor
On tracking portfolios with certainty equivalents on a generalization of Markowitz model: the Fool, the Wise and the AdaptiveRichard Nock; Brice Magdalou; Eric Briys; Frank Nielsen
Beat the Mean BanditYisong Yue; Thorsten Joachims
Multiclass Classification with Bandit Feedback using Adaptive RegularizationKoby Crammer; Claudio Gentile
2IStructured Output

Mehryar Mohri
An Augmented Lagrangian Approach to Constrained MAP InferenceAndre Martins; Mario Figueiredo; Pedro Aguiar; Noah Smith; Eric Xing
Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random FieldsStephen Gould
Inference of Inversion Transduction GrammarsAlexander Clark
Minimal Loss Hashing for Compact Binary CodesMohammad Norouzi; David Fleet
2EReinforcement Learning

Ron Parr
Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function's In-DegreeDoran Chakraborty; Peter Stone
The Infinite Regionalized Policy RepresentationMiao Liu; Xuejun Liao; Lawrence Carin
Online Discovery of Feature DependenciesAlborz Geramifard; Finale Doshi; Joshua Redding; Nicholas Roy; Jonathan How
Doubly Robust Policy Evaluation and LearningMiroslav Dudik; John Langford; Lihong Li
2FGraphical Models and Optimization

Nando de Freitas
Dynamic Tree Block Coordinate AscentDaniel Tarlow; Dhruv Batra; Pushmeet Kohli; Vladimir Kolmogorov
Approximation Bounds for Inference using Cooperative CutsStefanie Jegelka; Jeff Bilmes
Convex Max-Product over Compact Sets for Protein FoldingJian Peng; Tamir Hazan; David McAllester; Raquel Urtasun
On the Use of Variational Inference for Learning Discrete Graphical ModelsEunho Yang; Pradeep Ravikumar
2GRecommendation and Matrix Factorization

Dale Schuurmans
GoDec: Randomized Low-rank & Sparse Matrix Decomposition in Noisy CaseTianyi Zhou; Dacheng Tao
Large-Scale Convex Minimization with a Low-Rank ConstraintShai Shalev-Shwartz; Alon Gonen; Ohad Shamir
Linear Regression under Fixed-Rank Constraints: A Riemannian ApproachGilles Meyer; Silvère Bonnabel; Rodolphe Sepulchre
Clustering by Left-Stochastic Matrix Factorization Raman Arora; Maya Gupta; Amol Kapila; Maryam Fazel,
Wed, 12.10-1.40 Lunch Break
MLJ Editorial Board LuncheonMachine Learning Journal
Wed, 1.40-3.20 3ANeural Networks and Statistical Methods

Thore Graepel
Minimum Probability Flow LearningJascha Sohl-Dickstein; Peter Battaglino; Michael DeWeese
The Importance of Encoding Versus Training with Sparse Coding and Vector QuantizationAdam Coates; Andrew Ng
Learning Recurrent Neural Networks with Hessian-Free OptimizationJames Martens; Ilya Sutskever
On Random Weights and Unsupervised Feature LearningAndrew Saxe; Pang Wei Koh; Zhenghao Chen; Maneesh Bhand; Bipin Suresh; Andrew Ng
3ILatent-Variable Models

Alexander Ihler
On the Integration of Topic Modeling and Dictionary LearningLingbo Li; Mingyuan Zhou; Guillermo Sapiro; Lawrence Carin
Beam Search based MAP Estimates for the Indian Buffet ProcessPiyush Rai; Hal Daume III
Tree-Structured Infinite Sparse Factor ModelXianXing Zhang; David Dunson; Lawrence Carin
Sparse Additive Generative Models of TextJacob Eisenstein; Amr Ahmed; Eric Xing
3ELarge-Scale Learning

Rich Caruana
Hashing with GraphsWei Liu; Jun Wang; Sanjiv Kumar; Shih-Fu Chang
Large Scale Text Classification using Semi-supervised Multinomial Naive BayesJiang Su; Jelber Sayyad Shirab; Stan Matwin
Parallel Coordinate Descent for L1-Regularized Loss MinimizationJoseph Bradley; Aapo Kyrola; Daniel Bickson; Carlos Guestrin
OptiML: An Implicitly Parallel Domain-Specific Language for Machine LearningArvind Sujeeth; HyoukJoong Lee; Kevin Brown; Tiark Rompf; Hassan Chafi; Michael Wu; Anand Atreya; Martin Odersky; Kunle Olukotun
3FLearning Theory

Sally Goldman
On the Necessity of Irrelevant VariablesDave Helmbold; Phil Long
A PAC-Bayes Sample-compression Approach to Kernel MethodsPascal Germain; Alexandre Lacoste; Francois Laviolette; Mario Marchand; Sara Shanian
Simultaneous Learning and Covering with Adversarial NoiseAndrew Guillory; Jeff Bilmes
Risk-Based Generalizations of f-divergencesDarío García-García; Ulrike von Luxburg; Raúl Santos-Rodríguez
3GFeature Selection, Dimensionality Reduction

Corinna Cortes
Eigenvalue Sensitive Feature SelectionYi Jiang; Jiangtao Ren
Cauchy Graph EmbeddingDijun Luo; Chris Ding; Feiping Nie; Heng Huang
Tree preserving embeddingAlbert Shieh; Tatsunori Hashimoto; Edo Airoldi
Stochastic Low-Rank Kernel Learning for RegressionPierre Machart; Thomas Peel; Sandrine Anthoine; Liva Ralaivola; Hervé Glotin,
Wed, 3.20-3.50 Coffee Break
Wed, 3.50-5.30 4AInvited Cross-Conference Track

Dragos Margineantu
Debt Collections Using Constrained Reinforcement LearningNaoki Abe; Prem Melville; Cezar Pendus; David L. Jensen; Chandan K. Reddy; Vince P. Thomas; James J. Bennett; Gary F. Anderson; Brent R. Cooley; Melissa Weatherwax; Timothy Gardinier; Gerard Miller
Modeling Mutual Context of Object and Human Pose in Human-Object Interaction ActivitiesBangpeng Yao; Aditya Khosla; Li Fei-Fei
Efficient Planning under Uncertainty for a Target-Tracking Micro-Aerial Vehicle in Urban EnvironmentsAbraham Bachrach; Ruijie He; Nicholas Roy
Gesture-Based Human-Robot Jazz ImprovisationGil Weinberg
4INeural Networks and Deep Learning

Tomas Singliar
Learning attentional policies for tracking and recognition in video with deep networksLoris Bazzani; Nando Freitas; Hugo Larochelle; Vittorio Murino; Jo-Anne Ting
Learning Deep Energy ModelsJiquan Ngiam; Zhenghao Chen; Pang Wei Koh; Andrew Ng
Unsupervised Models of Images by Spike-and-Slab RBMsAarron Courville; James Bergstra; Yoshua Bengio
On Autoencoders and Score Matching for Energy Based ModelsKevin Swersky; Marc'Aurelio Ranzato; David Buchman; Benjamin Marlin; Nando Freitas
4ELatent-Variable Models

Katherine Heller
Topic Modeling with Nonparametric Markov TreeHaojun Chen; David Dunson; Lawrence Carin
Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel MachinesJun Zhu; Ning Chen; Eric Xing
Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian ModelsBenjamin Marlin*, University of British Columbia; Mohammad Khan, University of British Columbia; Kevin Murphy, University of British Columbia
A Spectral Algorithm for Latent Tree Graphical ModelsAnkur Parikh; Le Song; Eric Xing
4FActive and Online Learning

Burr Settles
Speeding-Up Hoeffding-Based Regression Trees With Options Elena Ikonomovska; João Gama; Bernard Zenko; Saso Dzeroski
Adaptively Learning the Crowd KernelOmer Tamuz; Ce Liu; Serge Belongie; Ohad Shamir; Adam Kalai
Bundle Selling by Online Estimation of Valuation FunctionsDaniel Vainsencher; Ofer Dekel; Shie Mannor
Active Learning from CrowdsYan Yan; Romer Rosales; Glenn Fung; Jennifer Dy
4GEnsemble Methods

Chris Burges
Efficient Rule Ensemble Learning using Hierarchical KernelsPratik Jawanpuria; Saketha Nath Jagarlapudi; Ganesh Ramakrishnan
Boosting on a Budget: Sampling for Feature-Efficient PredictionLev Reyzin
Multiclass Boosting with Hinge Loss based on Output CodingTianshi Gao; Daphne Koller
Generalized Boosting Algorithms for Convex OptimizationAlexander Grubb; Drew Bagnell
Wed, 5.30-6 5ATest-of-Time

Tom Dietterich
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataJohn D. Lafferty; Andrew McCallum; Fernando C. N. Pereira
Wed, 6-10 EvgPoster SessionPapers from Sessions 2A-7G - Evergreen Balroom

Thu, 30 June

TimeSessionSession Title
Session Chair
Paper TitleAuthors
Thu, 8:30-9 6ABest Paper

Lise Getoor and Tobias Scheffer
Computational Rationalization: The Inverse Equilibrium ProblemKevin Waugh; Brian Ziebart; Drew Bagnell
Thu, 9-10 6AKeynote

Lise Getoor
Evolutionary dynamics of competition and cooperationMartin Nowak
Thu, 10-10.30 Coffee Break
Thu, 10.30-12.10 7ARobotics and Reinforcement Learning

Joelle Pineau
Conjugate Markov Decision ProcessesPhilip Thomas; Andrew Barto
Approximate Dynamic Programming for Storage ProblemsLauren Hannah; David Dunson
Apprenticeship Learning About Multiple IntentionsMonica Babes; Vukosi Marivate; Michael Littman; Kaushik Subramanian
Classification-based Policy Iteration with a CriticVictor Gabillon; Alessandro Lazaric; Mohammad Ghavamzadeh; Bruno Scherrer
7ITransfer Learning

Kilian Weinberger
A Graph-based Framework for Multi-Task Multi-View LearningJingrui He; Rick Lawrence
Learning from Multiple OutlooksMaayan Harel; Shie Mannor
Learning with Whom to Share in Multi-task Feature LearningZhuoliang Kang; Kristen Grauman; Fei Sha
Hierarchical Classification via Orthogonal TransferLin Xiao; Dengyong Zhou; Mingrui Wu
7EKernel Methods

Olivier Chapelle
BCDNPKL: Scalable Non-Parametric Kernel Learning Using Block Coordinate DescentEn-Liang Hu; Bo Wang; SongCan Chen
Ultra-Fast Optimization Algorithm for Sparse Multi Kernel LearningFrancesco Orabona; Luo Jie
Fast Global Alignment KernelsMarco Cuturi
Mapping kernels for treesKilho Shin; Marco Cuturi; Tetsuji Kuboyama

Jeff Bilmes
Fast Newton-type Methods for Total Variation RegularizationÁlvaro Barbero; Suvrit Sra
The Constrained Weight Space SVM: Learning with Ranked FeaturesKevin Small; Byron Wallace; Carla Brodley; Thomas Trikalinos
Size-constrained Submodular Minimization through Minimum Norm BaseKiyohito Nagano; Yoshinobu Kawahara; Kazuyuki Aihara
Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online LearningSangkyun Lee; Stephen Wright
7GLearning Theory

Nicolo Cesa-Bianchi
Multiple Instance Learning with Manifold BagsBoris Babenko; Nakul Verma; Piotr Dollar; Serge Belongie
Minimax Learning Rates for Bipartite Ranking and Plug-in RulesSylvain Robbiano; Stéphan Clémençon
From PAC-Bayes Bounds to Quadratic Programs for Majority VotesJean-Francis Roy; Francois Laviolette; Mario Marchand
Thu, 12.10-1.40 Women in Machine Learning LuncheonWIMLAll women in ML are invited to register
Lunch Break
Thu, 1.40-3.20 8AInvited Cross-Conference Session

Prem Melville
High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictionsChristina Leslie
Suggesting Friends Using the Implicit Social GraphMaayan Roth; Tzvika Barenholz; Assaf Ben-David; David Deutscher; Guy Flysher; Avinatan Hassidim; Ilan Horn; Ari Leichtberg; Naty Leiser; Yossi Matias; Ron Merom
Relevance and ranking in online dating systemsFernando Diaz; Donald Metzler; Sihem Amer-Yahia
We Just Clicked - Conversational Features of Social Bonding in Speed DatesRajesh Ranganath; Dan Jurafsky; Dan McFarland
8INeural Networks and Deep Learning

Yoshua Bengio
Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann MachinesKyungHyun Cho; Tapani Raiko; Alexander Ilin
On optimization methods for deep learningQuoc Le; Jiquan Ngiam; Adam Coates; Abhik Lahiri; Bobby Prochnow; Andrew Ng
The Hierarchical Beta Process for Convolutional Factor Analysis and Deep LearningBo Chen; Gungor Polatkan; Guillermo Sapiro; David Dunson; Lawrence Carin
Multimodal Deep LearningJiquan Ngiam; Aditya Khosla; Mingyu Kim; Juhan Nam; Honglak Lee; Andrew Ng
8EReinforcement Learning

Prasad Tadepalli
Mean-Variance Optimization in Markov Decision ProcessesShie Mannor; John Tsitsiklis
Incremental Basis Construction from Temporal Difference ErrorYi Sun; Faustino Gomez; Mark Ring; Jürgen Schmidhuber
Variational Inference for Policy Search in changing situationsGerhard Neumann
Finite-Sample Analysis of Lasso-TDMohammad Ghavamzadeh; Alessandro Lazaric; Remi Munos; Matthew Hoffman
8FBayesian Inference and Probabilistic Models

Andrew Ng
Estimating the Bayes Point Using Linear Knapsack ProblemsBrian Potetz
Message Passing Algorithms for the Dirichlet Diffusion TreeDavid Knowles; Jurgen Van Gael; Zoubin Ghahramani
Variational Inference for Stick-Breaking Beta Process PriorsJohn Paisley; Lawrence Carin; David Blei
Infinite Dynamic Bayesian NetworksFinale Doshi; David Wingate; Josh Tenenbaum; Nicholas Roy
8GSupervised Learning

Alexandru Niculescu-Mizil
Multi-Label Classification on Tree- and DAG-Structured HierarchiesWei Bi; James Kwok
Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costsClayton Scott
Support Vector Machines as Probabilistic ModelsVojtech Franc; Alexander Zien; Bernhard Schölkopf
Locally Linear Support Vector MachinesLubor Ladicky; Philip Torr
Thu, 3.20-3.50 Coffee Break
Thu, 3.50-4.40 9ASocial Networks

Alice Zheng
Uncovering the Temporal Dynamics of Diffusion NetworksManuel Gomez Rodriguez; David Balduzzi; Bernhard Schölkopf
Dynamic Egocentric Models for Citation NetworksDuy Vu; Arthur Asuncion; David Hunter; Padhraic Smyth
9IEvaluation Metrics

Tomas Singliar
Brier Curves: a New Cost-Based Visualisation of Classifier PerformanceJose Hernandez-Orallo; Peter Flach; Cèsar Ferri
A Coherent Interpretation of AUC as a Measure of Aggregated Classification PerformancePeter Flach; Jose Hernandez-Orallo; Cèsar Ferri
9Estatistical relational learning

Pedro Domingos
Relational Active Learning for Joint Collective Classification ModelsAnkit Kuwadekar; Jennifer Neville
A Three-Way Model for Collective Learning on Multi-Relational DataMaximilian Nickel; Volker Tresp; Hans-Peter Kriegel
9FOutlier Detection

Jennifer Dy
Learning Multi-View Neighborhood Preserving ProjectionsNovi Quadrianto; Christoph Lampert
On the Robustness of Kernel Density M-EstimatorsJooSeuk Kim; Clayton Scott
9GTime Series

Masashi Sugiyama
Time Series Clustering: Complex is Simpler!Lei Li; B. Aditya Prakash
Learning Discriminative Fisher KernelsLaurens Van der Maaten
Thu, 4:50pm Buses leave for the banquet

Fri, 1 July

TimeSessionSession Title
Session Chair
Paper TitleAuthors
Fri, 8.30-9.30 10AKeynote

Tobias Scheffer
Machine Learning in Google GogglesHartmut Neven
Fri, 9.30-10 Coffee Break
Fri, 10-12.10 11AGraphical Models and Bayesian Inference

Pradeep Ravikumar
Variational Heteroscedastic Gaussian Process RegressionMiguel Lazaro-Gredilla; Michalis Titsias
Predicting Legislative Roll Calls from TextSean Gerrish; David Blei
Bounding the Partition Function using Holder's InequalityQiang Liu; Alexander Ihler
On Bayesian PCA: Automatic Dimensionality Selection and Analytic SolutionShinichi Nakajima; Masashi Sugiyama; Derin Babacan
Bayesian CCA via Group SparsitySeppo Virtanen; Arto Klami; Samuel Kaski
11ISparsity and Compressed Sensing

Nati Srebro
Efficient Sparse Modeling with Automatic Feature GroupingWenliang Zhong; James Kwok
Robust Matrix Completion and Corrupted ColumnsYudong Chen; Huan Xu; Constantine Caramanis; Sujay Sanghavi
Clustering Partially Observed Graphs via Convex OptimizationAli Jalali; Yudong Chen; Sujay Sanghavi; Huan Xu
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensionsAlekh Agarwal; Sahand Negahban; Martin Wainwright
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary SelectionAbhimanyu Das; David Kempe

Jennifer Neville
On Information-Maximization Clustering: Tuning Parameter Selection and Analytic SolutionMasashi Sugiyama; Makoto Yamada; Manabu Kimura; Hirotaka Hachiya
Pruning nearest neighbor cluster treesSamory Kpotufe; Ulrike von Luxburg
A Co-training Approach for Multi-view Spectral ClusteringAbhishek Kumar; Hal Daume III
Clusterpath: an Algorithm for Clustering using Convex Fusion PenaltiesToby Hocking; Jean-Philippe Vert; Francis Bach; Armand Joulin
A Unified Probabilistic Model for Global and Local Unsupervised Feature SelectionYue Guan; Jennifer Dy; Michael Jordan
11FGame Theory and Planning and Control

Shie Mannor
Integrating Partial Model Knowledge in Model Free RL AlgorithmsAviv Tamar; Dotan Di Castro; Ron Meir
Task Space Retrieval Using Inverse Feedback ControlNikolay Jetchev; Marc Toussaint
PILCO: A Model-Based and Data-Efficient Approach to Policy SearchMarc Deisenroth; Carl Rasmussen
Approximating Correlated Equilibria using Relaxations on the Marginal PolytopeHetunandan Kamisetty; Eric Xing; Christopher Langmead
Generalized Value Functions for Large Action SetsJason Pazis; Ron Parr
11GSemi-Supervised Learning

William Cohen
Vector-valued Manifold RegularizationHa Quang Minh; Vikas Sindhwani
Semi-supervised Penalized Output Kernel Regression for Link PredictionCéline Brouard; Florence D'Alche-Buc; Marie Szafranski
Access to Unlabeled Data can Speed up Prediction TimeRuth Urner; Shai Shalev-Shwartz; Shai Ben-David
Automatic Feature Decomposition for Single View Co-trainingMinmin Chen; Kilian Weinberger; Yixin Chen
Towards Making Unlabeled Data Never HurtYu-Feng Li; Zhi-Hua Zhou
Fri, 12.10-1.40 Lunch Break
IMLS Board LuncheonIMLS Board Members
Fri, 1.40-3.45 12AKernel Methods and Optimization

Thorsten Joachims
Learning Output Kernels with Block Coordinate DescentFrancesco Dinuzzo; Cheng Soon Ong; Peter Gehler; Gianluigi Pillonetto
Implementing regularization implicitly via approximate eigenvector computationMichael Mahoney; Lorenzo Orecchia
Adaptive Kernel Approximation for Large-Scale Non-Linear SVM PredictionMichele Cossalter; Rong Yan; Lu Zheng
Suboptimal Solution Path Algorithm for Support Vector MachineMasayuki Karasuyama; Ichiro Takeuchi
Functional Regularized Least Squares Classication with Operator-valued KernelsHachem Kadri; Asma Rabaoui; Philippe Preux; Emmanuel Duflos; Alain Rakotomamonjy
12INeural Networks and NLP

Hal Daume III
Parsing Natural Scenes and Natural Language with Recursive Neural NetworksRichard Socher; Cliff Chiung-Yu Lin; Andrew Ng; Chris Manning
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning ApproachXavier Glorot; Antoine Bordes; Yoshua Bengio
Large-Scale Learning of Embeddings with Reconstruction SamplingYann Dauphin; Xavier Glorot; Yoshua Bengio
Generating Text with Recurrent Neural NetworksIlya Sutskever; James Martens; Geoffrey Hinton
Contractive Auto-Encoders: Explicit Invariance During Feature ExtractionSalah Rifai; Pascal Vincent; Xavier Muller; Xavier Glorot; Yoshua Bengio
12EProbabilistic Models & MCMC

Ruslan Salakhutdinov
Probabilistic Matrix AdditionAmrudin Agovic; Arindam Banerjee; Snigdhansu Chatterje
SampleRank: Training Factor Graphs with Atomic GradientsMichael Wick; Khashayar Rohanimanesh; Kedar Bellare; Aron Culotta; Andrew McCallum
A New Bayesian Rating System for Team CompetitionsSergey Nikolenko; Alexander Sirotkin
Bayesian Learning via Stochastic Gradient Langevin DynamicsMax Welling; Yee Whye Teh
ABC-EP: Expectation Propagation for Likelihood-free Bayesian ComputationSimon Barthelmé; Nicolas Chopin
12FOnline Learning

Claudio Gentile
Online AUC MaximizationPeilin Zhao; Steven Hoi; Rong Jin; Tianbao Yang
Online Submodular Minimization for Combinatorial StructuresStefanie Jegelka; Jeff Bilmes
Better Algorithms for Selective SamplingFrancesco Orabona; Nicolò Cesa-Bianchi
Learning Linear Functions with Quadratic and Linear Multiplicative UpdatesTom Bylander
Optimal Distributed Online PredictionOfer Dekel; Ran Gilad-Bachrach; Ohad Shamir; Lin Xiao
12GRanking and Information Retrieval

Mikhail Bilenko
Learning Mallows Models with Pairwise PreferencesTyler Lu; Craig Boutilier
Preserving Personalized Pagerank in SubgraphsAndrea Vattani; Deepayan Chakrabarti; Maxim Gurevich
Learning Scoring Functions with Order-Preserving Losses and Standardized SupervisionDavid Buffoni; Clément Calauzenes; Patrick Gallinari; Nicolas Usunier
Bipartite Ranking through Minimization of Univariate LossWojciech Kotlowski; Krzysztof Dembczynski; Eyke Huellermeier
k-DPPs: Fixed-Size Determinantal Point ProcessesAlex Kulesza; Ben Taskar
Fri, 3.45-4.15 Coffee break
Fri, 4.15-5.15 13AKeynote

Ray Mooney
Building Watson: An Overview of the DeepQA ProjectDavid Ferrucci
Fri, 5.15-6.15 14ABusiness Meeting

Ray Mooney
Lise Getoor, Tobias Scheffer
Fri, 6-10 Poster SessionPapers from Sessions 8A-12G - Evergreen Balroom