We focus on the problem of efficient learning of dependency trees. Once grown, they can be used as a special case of a Bayesian network, for PDF approximation, and for many other u...
The current framework of reinforcement learning is based on maximizing the expected returns based on scalar rewards. But in many real world situations, tradeoffs must be made amon...
We present a technique for transforming classical approximation algorithms into constant-time algorithms that approximate the size of the optimal solution. Our technique is applic...
The optimization of the epoxy polymerization process involves a number of conflicting objectives and more than twenty decision parameters. In this paper, the problem is treated tr...
Kalyanmoy Deb, Kishalay Mitra, Rinku Dewri, Saptar...
In this paper we introduce executions of place/transition Petri nets with weighted inhibitor arcs (PTI-net) as enabled labeled stratified order structures (LSOs) and present a po...