Regularization plays a central role in the analysis of modern data, where non-regularized fitting is likely to lead to over-fitted models, useless for both prediction and interpre...
Motivated by the possibilities of applying deductive database technology for efficient query answering in description logics, we present a translation operator µ that transforms...
This paper describes STAGE, a learning approach to automatically improving search performance on optimization problems.STAGElearns an evaluation function which predicts the outcom...
Designing the dialogue strategy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing di...
Diane J. Litman, Michael S. Kearns, Satinder P. Si...
In this paper, we present an approach for measuring certain properties of synthetic optimization problems based on the assumed distribution of coefficient values. We show how to e...