Welcome Dinner

Welcome

Invited Talk: Satinder Singh
Rethinking State, Action, and Reward in Reinforcement Learning

Coffee

Ranking and Scoring Using Empirical Risk Minimization
Stéphan Clemencon, Gabor Lugosi, Nicolas Vayatis

Learnability of Bipartite Ranking Functions
Shivani Agarwal, Dan Roth

Stability and Generalization of Bipartite Ranking Algorithms
Shivani Agarwal, Partha Niyogi

Loss Bounds for Online Category Ranking
Koby Crammer, Yoram Singer

Margin-Based Ranking meets Boosting in the middle
Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire

Martingale Boosting
Phil Long, Rocco A. Servedio

The Value of Agreement, A New Boosting Algorithm
Boaz Leskes

Coffee

A PAC-style Model for Learning from Labeled and Unlabeled Data
Maria Florina Balcan, Avrim Blum

Generalization Error Bounds Using Unlabeled Data
Matti Kääriäinen

On the Consistency of Multiclass Classification Methods
Ambuj Tewari, Peter Bartlett

Sensitive Error Correcting Output Codes
John Langford, Alina Beygelzimer

Google garden buffet

Data Dependent Concentration Bounds for Sequential Prediction algorithms
Tong Zhang

The Weak Aggregating Algorithm and Weak Mixability
Yuri Kalnishkan, Michael Vyugin

Tracking the best of many experts
Andras Gyorgy, Tamas Linder, Gabor Lugosi

Improved Second-Order Bounds for Prediction with Expert Advice
Nicolò Cesa-Bianchi, Yishay Mansour, Gilles Stoltz

Competitive Collaborative Learning
Baruch Awerbuch, Robert Kleinberg

Analysis of perceptron-based active learning
Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni

A New Perspective of an Old Perceptron Algorithm
Shai Shalev-Shwartz, Yoram Singer

Lunch

Fast Rates for Support Vector Machines
Ingo Steinwart, Clint Scovel

Exponential Convergence Rates in Classification
Vladimir Koltchinskii, Olexandra Beznosova

General Polynomial Time Decomposition Algorithms
Nikolas List, Hans Simon

Coffee

Approximating a Gram Matrix for Improved Kernel-Based Learning
Michael Mahoney, Petros Drineas

Learning convex combinations of continuously parameterized basic kernels
Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil

The Spectral Method for General Mixture Models
Ravi Kannan, Hadi Salmasian, Santosh Vempala

On Spectral Learning of Mixtures of Distributions
Dimitris Achlioptas, Frank McSherry

From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians
Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg

Invited Talk: Sergiu Hart
Uncoupled Dynamics and Nash Equilibrium

Permutation Tests for Classification
Polina Golland, Feng Liang, Sayan Mukherjee, Dmitry Panchenko

Localized Upper and Lower Bounds for Some Estimation Problems
Tong Zhang

Improved minimax bounds on the test and training distortion of empirically designed vector quantizers
András Antos

Rank, Trace-Norm and Max-Norm
Nathan Srebro, Adi Shraibman

Learning a Hidden Hypergraph
Dana Angluin, Jiang Chenn

On Attribute Efficient and Non-adaptive Learning of Parities and DNF Expressions
Vitaly Feldman

Unlabeled Compression Schemes for Maximum Classes
Dima Kuzmin, Manfred Warmuth

Banquet

Variations on U-shaped learning
Lorenzo Carlucci, Sanjay Jain, Efim Kinber, Frank Stephan

Mind Change Efficient Learning
Oliver Schulte, Wei Luo

On a syntactic characterization of classification with a mind change bound
Eric Martin, Arun Sharma

Trading in Markovian Price Models
Sham Kakade, Michael Kearns

From External to Internal Regret
Avrim Blum, Yishai Mansour

Separating Models of Learning from Correlated and Uncorrelated Data
Andrew Wan, Ariel Elbaz, Homin K. Lee, Rocco A. Servedio