Hidden Markov models (HMMs) have received considerable attention in various communities (e.g, speech recognition, neurology and bioinformatic) since many applications that use HMM...
We study distribution-dependent, data-dependent, learning in the limit with adversarial disturbance. We consider an optimization-based approach to learning binary classifiers from...
In sequential decision making under uncertainty, as in many other modeling endeavors, researchers observe a dynamical system and collect data measuring its behavior over time. The...
We study data structures in the presence of adversarial noise. We want to encode a given object in a succinct data structure that enables us to efficiently answer specific queries...
We demonstrate an improved consensus-driven utility accrual scheduling algorithm (DUA-CLA) for distributable threads which execute under run-time uncertainties in execution time, ...
Jonathan Stephen Anderson, Binoy Ravindran, E. Dou...