COLT 2005

48 papers

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

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

Analysis of Perceptron-Based Active Learning Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni

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

Asymptotic Log-Loss of Prequential Maximum Likelihood Codes Peter Grünwald, Steven de Rooij

Competitive Collaborative Learning Baruch Awerbuch, Robert D. Kleinberg

Compute Inclusion Depth of a Pattern Wei Luo

Data Dependent Concentration Bounds for Sequential Prediction Algorithms Tong Zhang

Ellipsoid Approximation Using Random Vectors Shahar Mendelson, Alain Pajor

Exponential Convergence Rates in Classification Vladimir Koltchinskii, Olexandra Beznosova

Fast Rates for Support Vector Machines Ingo Steinwart, Clint Scovel

From External to Internal Regret Avrim Blum, Yishay Mansour

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

General Polynomial Time Decomposition Algorithms Nikolas List, Hans Ulrich Simon

Generalization Error Bounds Using Unlabeled Data Matti Kääriäinen

Improved Minimax Bounds on the Test and Training Distortion of Empirically Designed Vector Quantizers András Antos

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

Learnability of Bipartite Ranking Functions Shivani Agarwal, Dan Roth

Learning a Hidden Hypergraph Dana Angluin, Jiang Chen

Learning Convex Combinations of Continuously Parameterized Basic Kernels Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil

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

Localized Upper and Lower Bounds for Some Estimation Problems Tong Zhang

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 Philip M. Long, Rocco A. Servedio

Mind Change Efficient Learning Wei Luo, Oliver Schulte

On a Syntactic Characterization of Classification with a Mind Change Bound Eric Martin, Arun Sharma

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

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

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

On the Limitations of Embedding Methods Shahar Mendelson

Optimum Follow the Leader Algorithm Dima Kuzmin, Manfred K. Warmuth

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

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

Ranking and Scoring Using Empirical Risk Minimization Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis

Sensitive Error Correcting Output Codes John Langford, Alina Beygelzimer

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

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

Teaching Classes with High Teaching Dimension Using Few Examples Frank J. Balbach

The Cross Validation Problem John Langford

The Spectral Method for General Mixture Models Ravindran Kannan, Hadi Salmasian, Santosh S. Vempala

The Value of Agreement, a New Boosting Algorithm Boaz Leskes

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

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

Tracking the Best of Many Experts András György, Tamás Linder, Gábor Lugosi

Trading in Markovian Price Models Sham M. Kakade, Michael J. Kearns

Unlabeled Compression Schemes for Maximum Classes, Dima Kuzmin, Manfred K. Warmuth

Variations on U-Shaped Learning Lorenzo Carlucci, Sanjay Jain, Efim B. Kinber, Frank Stephan