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COLT
2006
Springer
14 years 3 months ago
Online Learning with Constraints
In this paper, we study a sequential decision making problem. The objective is to maximize the total reward while satisfying constraints, which are defined at every time step. The...
Shie Mannor, John N. Tsitsiklis
COLT
2006
Springer
14 years 3 months ago
Online Learning with Variable Stage Duration
We consider online learning in repeated decision problems, within the framework of a repeated game against an arbitrary opponent. For repeated matrix games, well known results esta...
Shie Mannor, Nahum Shimkin
COLT
2006
Springer
14 years 3 months ago
Discriminative Learning Can Succeed Where Generative Learning Fails
Generative algorithms for learning classifiers use training data to separately estimate a probability model for each class. New items are classified by comparing their probabiliti...
Philip M. Long, Rocco A. Servedio
COLT
2006
Springer
14 years 3 months ago
Improving Random Projections Using Marginal Information
Abstract. We present an improved version of random projections that takes advantage of marginal norms. Using a maximum likelihood estimator (MLE), marginconstrained random projecti...
Ping Li, Trevor Hastie, Kenneth Ward Church
COLT
2006
Springer
14 years 3 months ago
DNF Are Teachable in the Average Case
We study the average number of well-chosen labeled examples that are required for a helpful teacher to uniquely specify a target function within a concept class. This "average...
Homin K. Lee, Rocco A. Servedio, Andrew Wan
COLT
2006
Springer
14 years 3 months ago
Optimal Oracle Inequality for Aggregation of Classifiers Under Low Noise Condition
We consider the problem of optimality, in a minimax sense, and adaptivity to the margin and to regularity in binary classification. We prove an oracle inequality, under the margin ...
Guillaume Lecué
COLT
2006
Springer
14 years 3 months ago
Uniform Convergence of Adaptive Graph-Based Regularization
Abstract. The regularization functional induced by the graph Laplacian of a random neighborhood graph based on the data is adaptive in two ways. First it adapts to an underlying ma...
Matthias Hein
COLT
2006
Springer
14 years 3 months ago
Logarithmic Regret Algorithms for Online Convex Optimization
In an online convex optimization problem a decision-maker makes a sequence of decisions, i.e., chooses a sequence of points in Euclidean space, from a fixed feasible set. After ea...
Elad Hazan, Adam Kalai, Satyen Kale, Amit Agarwal
COLT
2006
Springer
14 years 3 months ago
Online Multitask Learning
We study the problem of online learning of multiple tasks in parallel. On each online round, the algorithm receives an instance and makes a prediction for each one of the parallel ...
Ofer Dekel, Philip M. Long, Yoram Singer
COLT
2006
Springer
14 years 3 months ago
Efficient Learning Algorithms Yield Circuit Lower Bounds
We describe a new approach for understanding the difficulty of designing efficient learning algorithms. We prove that the existence of an efficient learning algorithm for a circui...
Lance Fortnow, Adam R. Klivans