We describe and analyze efficient algorithms for learning a linear predictor from examples when the learner can only view a few attributes of each training example. This is the ca...
We develop a new algorithm, based on EM, for learning the Linear Dynamical System model. Called the method of Approximated Second-Order Statistics (ASOS) our approach achieves dra...
We address the problem of evaluating the risk of a given model accurately at minimal labeling costs. This problem occurs in situations in which risk estimates cannot be obtained f...
Christoph Sawade, Niels Landwehr, Steffen Bickel, ...
Abstract--Reinforcement learning (RL) research typically develops algorithms for helping an RL agent best achieve its goals-however they came to be defined--while ignoring the rela...
We propose a novel approach that reduces cost-sensitive classification to one-sided regression. The approach stores the cost information in the regression labels and encodes the m...
Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
This paper experimentally evaluates multiagent learning algorithms playing repeated matrix games to maximize their cumulative return. Previous works assessed that Qlearning surpas...
Semi-supervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likeliho...
Joshua Dillon, Krishnakumar Balasubramanian, Guy L...