COLT 1997

35 papers

A Brief Look at Some Machine Learning Problems in Genomics David Haussler

A Dichotomy Theorem for Learning Quantified Boolean Formulas Víctor Dalmau

A PAC Analysis of a Bayesian Estimator John Shawe-Taylor, Robert C. Williamson

Agnostic Learning of Geometric Patterns (Extended Abstract) Sally A. Goldman, Stephen Kwek, Stephen D. Scott

Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation Michael J. Kearns, Dana Ron

An Efficient Extension to Mixture Techniques for Prediction and Decision Trees Fernando C. N. Pereira, Yoram Singer

Analysis of Two Gradient-Based Algorithms for On-Line Regression Nicolò Cesa-Bianchi

Asymmetric Team Learning Kalvis Apsitis, Rusins Freivalds, Carl H. Smith

Computational Sample Complexity Scott E. Decatur, Oded Goldreich, Dana Ron

Dense Shattering and Teaching Dimensions for Differentiable Families (Extended Abstract) Adam Kowalczyk

Derandomizing Stochastic Prediction Strategies V. G. Vovk

Distributed Cooperative Bayesian Learning Strategies Kenji Yamanishi

Estimation of Time-Varying Parameters in Statistical Models: An Optimization Approach Dimitris Bertsimas, David Gamarnik, John N. Tsitsiklis

FINite Learning Capabilities and Their Limits Robert P. Daley, Bala Kalyanasundaram

General Convergence Results for Linear Discriminant Updates Adam J. Grove, Nick Littlestone, Dale Schuurmans

Generalized Notions of Mind Change Complexity Arun Sharma, Frank Stephan, Yuri Ventsov

Generating All Maximal Independent Sets of Bounded-Degree Hypergraphs Nina Mishra, Leonard Pitt

Inferring Answers to Queries William I. Gasarch, Andrew C. Y. Lee

Information Theory in Probability, Statistics, Learning, and Neural Nets (Abstract) Andrew R. Barron

Learning Distributions from Random Walks Funda Ergün, Ravi Kumar, Ronitt Rubinfeld

Learning from Examples with Unspecified Attribute Values (Extended Abstract) Sally A. Goldman, Stephen Kwek, Stephen D. Scott

Learning Logic Programs by Using the Product Homomorphism Method Tamás Horváth, Robert H. Sloan, György Turán

Learning Markov Chains with Variable Memory Length from Noisy Output Dana Angluin, Miklós Csürös

Learning Probabilistically Consistent Linear Threshold Functions Tom Bylander

Learning with Maximum-Entropy Distributions Yishay Mansour, Mariano Schain

On the Complexity of Learning for a Spiking Neuron (Extended Abstract) Wolfgang Maass, Michael Schmitt

On-Line Evaluation and Prediction Using Linear Functions Philip M. Long

On-Line Learning and the Metrical Task System Problem Avrim Blum, Carl Burch

PAC Adaptive Control of Linear Systems Claude-Nicolas Fiechter

Performance Bounds for Nonlinear Time Series Prediction Ron Meir

Resource Bounded Next Value and Explanatory Identification: Learning Automata, Patterns and Polynomials On-Line Susanne Kaufmann, Frank Stephan

Some Label Efficient Learning Results David P. Helmbold, Sandra Panizza

Teachers, Learners and Black Boxes Dana Angluin, Martins Krikis

The Binary Exponentiated Gradient Algorithm for Learning Linear Functions Tom Bylander

Universal Portfolios with and Without Transaction Costs Avrim Blum, Adam Kalai