Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms co...
Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevi...
Transactional data are ubiquitous. Several methods, including frequent itemsets mining and co-clustering, have been proposed to analyze transactional databases. In this work, we p...
Yang Xiang, Ruoming Jin, David Fuhry, Feodor F. Dr...
Frequent-pattern mining has been studied extensively on scalable methods for mining various kinds of patterns including itemsets, sequences, and graphs. However, the bottleneck of...
We present a range of new results for testing properties of Boolean functions that are defined in terms of the Fourier spectrum. Broadly speaking, our results show that the propert...
Parikshit Gopalan, Ryan O'Donnell, Rocco A. Served...
Dataflow formalisms have provided designers of digital signal processing systems with optimizations and guarantees to arrive at quality prototypes quickly. As system complexity in...
William Plishker, Nimish Sane, Mary Kiemb, Kapil A...