Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonze...
Alexandre d'Aspremont, Francis R. Bach, Laurent El...
We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. ...
In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of usin...
To minimize the time to market and cost of new sub 0.25um process technologies and products, PDF Solutions, Inc., has developed a new comprehensive approach based on the use of pr...
Marko P. Chew, Sharad Saxena, Thomas F. Cobourn, P...
Clusters of workstations are becoming popular platforms for parallel computing, but performance on these systems is more complex and harder to predict than on traditional parallel...
Geetanjali Sampemane, Scott Pakin, Andrew A. Chien