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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

Banquet

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:

Track	Ballroom sections
A	Grand-ABCD
I	Grand-IJ
E	Grand-E
F	Grand-F
G	Grand-G

Wed, 29 June

Time	Session	Session Title Session Chair	Paper Title	Authors
Wed, 8.30-9	1A	Welcome	Welcome address and Best Paper Awards	Zoubin Ghahramani, Lise Getoor, Tobias Scheffer
Wed, 9-10	1A	Keynote John Platt	Embracing Uncertainty: Applied Machine Learning Comes of Age	Christopher Bishop
Wed, 10-10.30		Coffee Break
Wed, 10.30-12.10	2A	Bandits and Online Learning John Langford	Unimodal Bandits	Jia Yuan Yu; Shie Mannor
			On tracking portfolios with certainty equivalents on a generalization of Markowitz model: the Fool, the Wise and the Adaptive	Richard Nock; Brice Magdalou; Eric Briys; Frank Nielsen
			Beat the Mean Bandit	Yisong Yue; Thorsten Joachims
			Multiclass Classification with Bandit Feedback using Adaptive Regularization	Koby Crammer; Claudio Gentile
	2I	Structured Output Mehryar Mohri	An Augmented Lagrangian Approach to Constrained MAP Inference	Andre Martins; Mario Figueiredo; Pedro Aguiar; Noah Smith; Eric Xing
			Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields	Stephen Gould
			Inference of Inversion Transduction Grammars	Alexander Clark
			Minimal Loss Hashing for Compact Binary Codes	Mohammad Norouzi; David Fleet
	2E	Reinforcement Learning Ron Parr	Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function's In-Degree	Doran Chakraborty; Peter Stone
			The Infinite Regionalized Policy Representation	Miao Liu; Xuejun Liao; Lawrence Carin
			Online Discovery of Feature Dependencies	Alborz Geramifard; Finale Doshi; Joshua Redding; Nicholas Roy; Jonathan How
			Doubly Robust Policy Evaluation and Learning	Miroslav Dudik; John Langford; Lihong Li
	2F	Graphical Models and Optimization Nando de Freitas	Dynamic Tree Block Coordinate Ascent	Daniel Tarlow; Dhruv Batra; Pushmeet Kohli; Vladimir Kolmogorov
			Approximation Bounds for Inference using Cooperative Cuts	Stefanie Jegelka; Jeff Bilmes
			Convex Max-Product over Compact Sets for Protein Folding	Jian Peng; Tamir Hazan; David McAllester; Raquel Urtasun
			On the Use of Variational Inference for Learning Discrete Graphical Models	Eunho Yang; Pradeep Ravikumar
	2G	Recommendation and Matrix Factorization Dale Schuurmans	GoDec: Randomized Low-rank & Sparse Matrix Decomposition in Noisy Case	Tianyi Zhou; Dacheng Tao
			Large-Scale Convex Minimization with a Low-Rank Constraint	Shai Shalev-Shwartz; Alon Gonen; Ohad Shamir
			Linear Regression under Fixed-Rank Constraints: A Riemannian Approach	Gilles 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 Luncheon		Machine Learning Journal
Wed, 1.40-3.20	3A	Neural Networks and Statistical Methods Thore Graepel	Minimum Probability Flow Learning	Jascha Sohl-Dickstein; Peter Battaglino; Michael DeWeese
			The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization	Adam Coates; Andrew Ng
			Learning Recurrent Neural Networks with Hessian-Free Optimization	James Martens; Ilya Sutskever
			On Random Weights and Unsupervised Feature Learning	Andrew Saxe; Pang Wei Koh; Zhenghao Chen; Maneesh Bhand; Bipin Suresh; Andrew Ng
	3I	Latent-Variable Models Alexander Ihler	On the Integration of Topic Modeling and Dictionary Learning	Lingbo Li; Mingyuan Zhou; Guillermo Sapiro; Lawrence Carin
			Beam Search based MAP Estimates for the Indian Buffet Process	Piyush Rai; Hal Daume III
			Tree-Structured Infinite Sparse Factor Model	XianXing Zhang; David Dunson; Lawrence Carin
			Sparse Additive Generative Models of Text	Jacob Eisenstein; Amr Ahmed; Eric Xing
	3E	Large-Scale Learning Rich Caruana	Hashing with Graphs	Wei Liu; Jun Wang; Sanjiv Kumar; Shih-Fu Chang
			Large Scale Text Classification using Semi-supervised Multinomial Naive Bayes	Jiang Su; Jelber Sayyad Shirab; Stan Matwin
			Parallel Coordinate Descent for L1-Regularized Loss Minimization	Joseph Bradley; Aapo Kyrola; Daniel Bickson; Carlos Guestrin
			OptiML: An Implicitly Parallel Domain-Specific Language for Machine Learning	Arvind Sujeeth; HyoukJoong Lee; Kevin Brown; Tiark Rompf; Hassan Chafi; Michael Wu; Anand Atreya; Martin Odersky; Kunle Olukotun
	3F	Learning Theory Sally Goldman	On the Necessity of Irrelevant Variables	Dave Helmbold; Phil Long
			A PAC-Bayes Sample-compression Approach to Kernel Methods	Pascal Germain; Alexandre Lacoste; Francois Laviolette; Mario Marchand; Sara Shanian
			Simultaneous Learning and Covering with Adversarial Noise	Andrew Guillory; Jeff Bilmes
			Risk-Based Generalizations of f-divergences	Darío García-García; Ulrike von Luxburg; Raúl Santos-Rodríguez
	3G	Feature Selection, Dimensionality Reduction Corinna Cortes	Eigenvalue Sensitive Feature Selection	Yi Jiang; Jiangtao Ren
			Cauchy Graph Embedding	Dijun Luo; Chris Ding; Feiping Nie; Heng Huang
			Tree preserving embedding	Albert Shieh; Tatsunori Hashimoto; Edo Airoldi
			Stochastic Low-Rank Kernel Learning for Regression	Pierre Machart; Thomas Peel; Sandrine Anthoine; Liva Ralaivola; Hervé Glotin,
Wed, 3.20-3.50		Coffee Break
Wed, 3.50-5.30	4A	Invited Cross-Conference Track Dragos Margineantu	Debt Collections Using Constrained Reinforcement Learning	Naoki 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 Activities	Bangpeng Yao; Aditya Khosla; Li Fei-Fei
			Efficient Planning under Uncertainty for a Target-Tracking Micro-Aerial Vehicle in Urban Environments	Abraham Bachrach; Ruijie He; Nicholas Roy
			Gesture-Based Human-Robot Jazz Improvisation	Gil Weinberg
	4I	Neural Networks and Deep Learning Tomas Singliar	Learning attentional policies for tracking and recognition in video with deep networks	Loris Bazzani; Nando Freitas; Hugo Larochelle; Vittorio Murino; Jo-Anne Ting
			Learning Deep Energy Models	Jiquan Ngiam; Zhenghao Chen; Pang Wei Koh; Andrew Ng
			Unsupervised Models of Images by Spike-and-Slab RBMs	Aarron Courville; James Bergstra; Yoshua Bengio
			On Autoencoders and Score Matching for Energy Based Models	Kevin Swersky; Marc'Aurelio Ranzato; David Buchman; Benjamin Marlin; Nando Freitas
	4E	Latent-Variable Models Katherine Heller	Topic Modeling with Nonparametric Markov Tree	Haojun Chen; David Dunson; Lawrence Carin
			Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines	Jun Zhu; Ning Chen; Eric Xing
			Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models	Benjamin Marlin*, University of British Columbia; Mohammad Khan, University of British Columbia; Kevin Murphy, University of British Columbia
			A Spectral Algorithm for Latent Tree Graphical Models	Ankur Parikh; Le Song; Eric Xing
	4F	Active 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 Kernel	Omer Tamuz; Ce Liu; Serge Belongie; Ohad Shamir; Adam Kalai
			Bundle Selling by Online Estimation of Valuation Functions	Daniel Vainsencher; Ofer Dekel; Shie Mannor
			Active Learning from Crowds	Yan Yan; Romer Rosales; Glenn Fung; Jennifer Dy
	4G	Ensemble Methods Chris Burges	Efficient Rule Ensemble Learning using Hierarchical Kernels	Pratik Jawanpuria; Saketha Nath Jagarlapudi; Ganesh Ramakrishnan
			Boosting on a Budget: Sampling for Feature-Efficient Prediction	Lev Reyzin
			Multiclass Boosting with Hinge Loss based on Output Coding	Tianshi Gao; Daphne Koller
			Generalized Boosting Algorithms for Convex Optimization	Alexander Grubb; Drew Bagnell
Wed, 5.30-6	5A	Test-of-Time Tom Dietterich	Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data	John D. Lafferty; Andrew McCallum; Fernando C. N. Pereira
Wed, 6-10	Evg	Poster Session	Papers from Sessions 2A-7G - Evergreen Balroom

Thu, 30 June

Time	Session	Session Title Session Chair	Paper Title	Authors
Thu, 8:30-9	6A	Best Paper Lise Getoor and Tobias Scheffer	Computational Rationalization: The Inverse Equilibrium Problem	Kevin Waugh; Brian Ziebart; Drew Bagnell
Thu, 9-10	6A	Keynote Lise Getoor	Evolutionary dynamics of competition and cooperation	Martin Nowak
Thu, 10-10.30		Coffee Break
Thu, 10.30-12.10	7A	Robotics and Reinforcement Learning Joelle Pineau	Conjugate Markov Decision Processes	Philip Thomas; Andrew Barto
			Approximate Dynamic Programming for Storage Problems	Lauren Hannah; David Dunson
			Apprenticeship Learning About Multiple Intentions	Monica Babes; Vukosi Marivate; Michael Littman; Kaushik Subramanian
			Classification-based Policy Iteration with a Critic	Victor Gabillon; Alessandro Lazaric; Mohammad Ghavamzadeh; Bruno Scherrer
	7I	Transfer Learning Kilian Weinberger	A Graph-based Framework for Multi-Task Multi-View Learning	Jingrui He; Rick Lawrence
			Learning from Multiple Outlooks	Maayan Harel; Shie Mannor
			Learning with Whom to Share in Multi-task Feature Learning	Zhuoliang Kang; Kristen Grauman; Fei Sha
			Hierarchical Classification via Orthogonal Transfer	Lin Xiao; Dengyong Zhou; Mingrui Wu
	7E	Kernel Methods Olivier Chapelle	BCDNPKL: Scalable Non-Parametric Kernel Learning Using Block Coordinate Descent	En-Liang Hu; Bo Wang; SongCan Chen
			Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning	Francesco Orabona; Luo Jie
			Fast Global Alignment Kernels	Marco Cuturi
			Mapping kernels for trees	Kilho Shin; Marco Cuturi; Tetsuji Kuboyama
	7F	Optimization Jeff Bilmes	Fast Newton-type Methods for Total Variation Regularization	Álvaro Barbero; Suvrit Sra
			The Constrained Weight Space SVM: Learning with Ranked Features	Kevin Small; Byron Wallace; Carla Brodley; Thomas Trikalinos
			Size-constrained Submodular Minimization through Minimum Norm Base	Kiyohito Nagano; Yoshinobu Kawahara; Kazuyuki Aihara
			Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online Learning	Sangkyun Lee; Stephen Wright
	7G	Learning Theory Nicolo Cesa-Bianchi	Multiple Instance Learning with Manifold Bags	Boris Babenko; Nakul Verma; Piotr Dollar; Serge Belongie
			Minimax Learning Rates for Bipartite Ranking and Plug-in Rules	Sylvain Robbiano; Stéphan Clémençon
			From PAC-Bayes Bounds to Quadratic Programs for Majority Votes	Jean-Francis Roy; Francois Laviolette; Mario Marchand

Thu, 12.10-1.40		Women in Machine Learning Luncheon	WIML	All women in ML are invited to register
		Lunch Break
Thu, 1.40-3.20	8A	Invited Cross-Conference Session Prem Melville	High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions	Christina Leslie
			Suggesting Friends Using the Implicit Social Graph	Maayan 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 systems	Fernando Diaz; Donald Metzler; Sihem Amer-Yahia
			We Just Clicked - Conversational Features of Social Bonding in Speed Dates	Rajesh Ranganath; Dan Jurafsky; Dan McFarland
	8I	Neural Networks and Deep Learning Yoshua Bengio	Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines	KyungHyun Cho; Tapani Raiko; Alexander Ilin
			On optimization methods for deep learning	Quoc Le; Jiquan Ngiam; Adam Coates; Abhik Lahiri; Bobby Prochnow; Andrew Ng
			The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning	Bo Chen; Gungor Polatkan; Guillermo Sapiro; David Dunson; Lawrence Carin
			Multimodal Deep Learning	Jiquan Ngiam; Aditya Khosla; Mingyu Kim; Juhan Nam; Honglak Lee; Andrew Ng
	8E	Reinforcement Learning Prasad Tadepalli	Mean-Variance Optimization in Markov Decision Processes	Shie Mannor; John Tsitsiklis
			Incremental Basis Construction from Temporal Difference Error	Yi Sun; Faustino Gomez; Mark Ring; Jürgen Schmidhuber
			Variational Inference for Policy Search in changing situations	Gerhard Neumann
			Finite-Sample Analysis of Lasso-TD	Mohammad Ghavamzadeh; Alessandro Lazaric; Remi Munos; Matthew Hoffman
	8F	Bayesian Inference and Probabilistic Models Andrew Ng	Estimating the Bayes Point Using Linear Knapsack Problems	Brian Potetz
			Message Passing Algorithms for the Dirichlet Diffusion Tree	David Knowles; Jurgen Van Gael; Zoubin Ghahramani
			Variational Inference for Stick-Breaking Beta Process Priors	John Paisley; Lawrence Carin; David Blei
			Infinite Dynamic Bayesian Networks	Finale Doshi; David Wingate; Josh Tenenbaum; Nicholas Roy
	8G	Supervised Learning Alexandru Niculescu-Mizil	Multi-Label Classification on Tree- and DAG-Structured Hierarchies	Wei Bi; James Kwok
			Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costs	Clayton Scott
			Support Vector Machines as Probabilistic Models	Vojtech Franc; Alexander Zien; Bernhard Schölkopf
			Locally Linear Support Vector Machines	Lubor Ladicky; Philip Torr
Thu, 3.20-3.50		Coffee Break
Thu, 3.50-4.40	9A	Social Networks Alice Zheng	Uncovering the Temporal Dynamics of Diffusion Networks	Manuel Gomez Rodriguez; David Balduzzi; Bernhard Schölkopf
			Dynamic Egocentric Models for Citation Networks	Duy Vu; Arthur Asuncion; David Hunter; Padhraic Smyth
	9I	Evaluation Metrics Tomas Singliar	Brier Curves: a New Cost-Based Visualisation of Classifier Performance	Jose Hernandez-Orallo; Peter Flach; Cèsar Ferri
			A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance	Peter Flach; Jose Hernandez-Orallo; Cèsar Ferri
	9E	statistical relational learning Pedro Domingos	Relational Active Learning for Joint Collective Classification Models	Ankit Kuwadekar; Jennifer Neville
			A Three-Way Model for Collective Learning on Multi-Relational Data	Maximilian Nickel; Volker Tresp; Hans-Peter Kriegel
	9F	Outlier Detection Jennifer Dy	Learning Multi-View Neighborhood Preserving Projections	Novi Quadrianto; Christoph Lampert
			On the Robustness of Kernel Density M-Estimators	JooSeuk Kim; Clayton Scott
	9G	Time Series Masashi Sugiyama	Time Series Clustering: Complex is Simpler!	Lei Li; B. Aditya Prakash
			Learning Discriminative Fisher Kernels	Laurens Van der Maaten
Thu, 4:50pm		Buses leave for the banquet

Fri, 1 July

Time	Session	Session Title Session Chair	Paper Title	Authors
Fri, 8.30-9.30	10A	Keynote Tobias Scheffer	Machine Learning in Google Goggles	Hartmut Neven
Fri, 9.30-10		Coffee Break
Fri, 10-12.10	11A	Graphical Models and Bayesian Inference Pradeep Ravikumar	Variational Heteroscedastic Gaussian Process Regression	Miguel Lazaro-Gredilla; Michalis Titsias
			Predicting Legislative Roll Calls from Text	Sean Gerrish; David Blei
			Bounding the Partition Function using Holder's Inequality	Qiang Liu; Alexander Ihler
			On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution	Shinichi Nakajima; Masashi Sugiyama; Derin Babacan
			Bayesian CCA via Group Sparsity	Seppo Virtanen; Arto Klami; Samuel Kaski
	11I	Sparsity and Compressed Sensing Nati Srebro	Efficient Sparse Modeling with Automatic Feature Grouping	Wenliang Zhong; James Kwok
			Robust Matrix Completion and Corrupted Columns	Yudong Chen; Huan Xu; Constantine Caramanis; Sujay Sanghavi
			Clustering Partially Observed Graphs via Convex Optimization	Ali Jalali; Yudong Chen; Sujay Sanghavi; Huan Xu
			Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions	Alekh Agarwal; Sahand Negahban; Martin Wainwright
			Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection	Abhimanyu Das; David Kempe
	11E	Clustering Jennifer Neville	On Information-Maximization Clustering: Tuning Parameter Selection and Analytic Solution	Masashi Sugiyama; Makoto Yamada; Manabu Kimura; Hirotaka Hachiya
			Pruning nearest neighbor cluster trees	Samory Kpotufe; Ulrike von Luxburg
			A Co-training Approach for Multi-view Spectral Clustering	Abhishek Kumar; Hal Daume III
			Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties	Toby Hocking; Jean-Philippe Vert; Francis Bach; Armand Joulin
			A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection	Yue Guan; Jennifer Dy; Michael Jordan
	11F	Game Theory and Planning and Control Shie Mannor	Integrating Partial Model Knowledge in Model Free RL Algorithms	Aviv Tamar; Dotan Di Castro; Ron Meir
			Task Space Retrieval Using Inverse Feedback Control	Nikolay Jetchev; Marc Toussaint
			PILCO: A Model-Based and Data-Efficient Approach to Policy Search	Marc Deisenroth; Carl Rasmussen
			Approximating Correlated Equilibria using Relaxations on the Marginal Polytope	Hetunandan Kamisetty; Eric Xing; Christopher Langmead
			Generalized Value Functions for Large Action Sets	Jason Pazis; Ron Parr
	11G	Semi-Supervised Learning William Cohen	Vector-valued Manifold Regularization	Ha Quang Minh; Vikas Sindhwani
			Semi-supervised Penalized Output Kernel Regression for Link Prediction	Céline Brouard; Florence D'Alche-Buc; Marie Szafranski
			Access to Unlabeled Data can Speed up Prediction Time	Ruth Urner; Shai Shalev-Shwartz; Shai Ben-David
			Automatic Feature Decomposition for Single View Co-training	Minmin Chen; Kilian Weinberger; Yixin Chen
			Towards Making Unlabeled Data Never Hurt	Yu-Feng Li; Zhi-Hua Zhou
Fri, 12.10-1.40		Lunch Break
		IMLS Board Luncheon		IMLS Board Members
Fri, 1.40-3.45	12A	Kernel Methods and Optimization Thorsten Joachims	Learning Output Kernels with Block Coordinate Descent	Francesco Dinuzzo; Cheng Soon Ong; Peter Gehler; Gianluigi Pillonetto
			Implementing regularization implicitly via approximate eigenvector computation	Michael Mahoney; Lorenzo Orecchia
			Adaptive Kernel Approximation for Large-Scale Non-Linear SVM Prediction	Michele Cossalter; Rong Yan; Lu Zheng
			Suboptimal Solution Path Algorithm for Support Vector Machine	Masayuki Karasuyama; Ichiro Takeuchi
			Functional Regularized Least Squares Classication with Operator-valued Kernels	Hachem Kadri; Asma Rabaoui; Philippe Preux; Emmanuel Duflos; Alain Rakotomamonjy
	12I	Neural Networks and NLP Hal Daume III	Parsing Natural Scenes and Natural Language with Recursive Neural Networks	Richard Socher; Cliff Chiung-Yu Lin; Andrew Ng; Chris Manning
			Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach	Xavier Glorot; Antoine Bordes; Yoshua Bengio
			Large-Scale Learning of Embeddings with Reconstruction Sampling	Yann Dauphin; Xavier Glorot; Yoshua Bengio
			Generating Text with Recurrent Neural Networks	Ilya Sutskever; James Martens; Geoffrey Hinton
			Contractive Auto-Encoders: Explicit Invariance During Feature Extraction	Salah Rifai; Pascal Vincent; Xavier Muller; Xavier Glorot; Yoshua Bengio
	12E	Probabilistic Models & MCMC Ruslan Salakhutdinov	Probabilistic Matrix Addition	Amrudin Agovic; Arindam Banerjee; Snigdhansu Chatterje
			SampleRank: Training Factor Graphs with Atomic Gradients	Michael Wick; Khashayar Rohanimanesh; Kedar Bellare; Aron Culotta; Andrew McCallum
			A New Bayesian Rating System for Team Competitions	Sergey Nikolenko; Alexander Sirotkin
			Bayesian Learning via Stochastic Gradient Langevin Dynamics	Max Welling; Yee Whye Teh
			ABC-EP: Expectation Propagation for Likelihood-free Bayesian Computation	Simon Barthelmé; Nicolas Chopin
	12F	Online Learning Claudio Gentile	Online AUC Maximization	Peilin Zhao; Steven Hoi; Rong Jin; Tianbao Yang
			Online Submodular Minimization for Combinatorial Structures	Stefanie Jegelka; Jeff Bilmes
			Better Algorithms for Selective Sampling	Francesco Orabona; Nicolò Cesa-Bianchi
			Learning Linear Functions with Quadratic and Linear Multiplicative Updates	Tom Bylander
			Optimal Distributed Online Prediction	Ofer Dekel; Ran Gilad-Bachrach; Ohad Shamir; Lin Xiao
	12G	Ranking and Information Retrieval Mikhail Bilenko	Learning Mallows Models with Pairwise Preferences	Tyler Lu; Craig Boutilier
			Preserving Personalized Pagerank in Subgraphs	Andrea Vattani; Deepayan Chakrabarti; Maxim Gurevich
			Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision	David Buffoni; Clément Calauzenes; Patrick Gallinari; Nicolas Usunier
			Bipartite Ranking through Minimization of Univariate Loss	Wojciech Kotlowski; Krzysztof Dembczynski; Eyke Huellermeier
			k-DPPs: Fixed-Size Determinantal Point Processes	Alex Kulesza; Ben Taskar
Fri, 3.45-4.15		Coffee break
Fri, 4.15-5.15	13A	Keynote Ray Mooney	Building Watson: An Overview of the DeepQA Project	David Ferrucci
Fri, 5.15-6.15	14A	Business Meeting Ray Mooney		Lise Getoor, Tobias Scheffer
Fri, 6-10		Poster Session	Papers from Sessions 8A-12G - Evergreen Balroom