• The Value of Agreement, A New Boosting Algorithm
  Boaz Leskes

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

• Learning a Hidden Hypergraph
  Dana Angluin, Jiang Chen

• Generalization Error Bounds Using Unlabeled Data
  Matti Kaariainen

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

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

• Fast Rates for Support Vector Machines
  Ingo Steinwart, Clint Scovel

• Ranking and scoring using empirical risk minimization
  Ste'phan Clemencon, Gabor Lugosi, Nicolas Vayatis

• Asymptotic Log-loss of Prequential Maximum Likelihood Codes
  Peter Grunwald, Steven de Rooij

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

• Improved Second-Order Bounds for Prediction with Expert Advice
  Nicol`o Cesa-Bianchi, Yishay Mansour, Gilles Stoltz

• General Polynomial Time Decomposition Algorithms
  Nikolas List, Hans Simon

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

• Martingale Boosting
  Phil Long, Rocco A. Servedio

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

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

• Leaving the Span
  Manfred Warmuth, S.V.N. Vishwanathan

• Data Dependent Concentration Bounds for Sequential Prediction Algorithms
  Tong Zhang

• Localized Upper and Lower Bounds for Some Estimation Problems
  Tong Zhang

• Exponential Convergence Rates in Classification
  Vladimir Koltchinskii, Olexandra Beznosova

• Approximation with random vectors
  Shahar Mendelson, Alain Pajor

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

• Learnability of Bipartite Ranking Functions
  Shivani Agarwal, Dan Roth

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

• On the limitations of embedding methods
  Shahar Mendelson

• Trading in Markovian Price Models
  Sham Kakade, Michael Kearns

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

• Teaching Classes with High Teaching Dimension
  Frank Balbach

• Unlabeled Compression Schemes for Maximum Classes
  Dima Kuzmin, Manfred Warmuth

• 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

• Sensitive Error Correcting Output Codes
  John Langford, Alina Beygelzimer

• 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

• Loss Bounds for Online Category Ranking
  Koby Crammer, Yoram Singer

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

• Improved minimax bounds on the test and training distortion of empirically designed vector quantizers
  Andra's Antos

• Mind Change Efficient Learning
  Oliver Schulte, Wei Luo

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

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

• Toward a Theoretical Foundation for Laplacian-Based Manifold Methods
  Mikhail Belkin, Partha Niyogi

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

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

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

• Competitive Collaborative Learning
  Baruch Awerbuch, Robert Kleinberg