In this paper, we consider approximation algorithms for optimizing a generic multi-variate homogeneous polynomial function, subject to homogeneous quadratic constraints. Such opti...
An important research thread in algorithmic game theory studies the design of efficient truthful mechanisms that approximate the optimal social welfare. A fundamental question is ...
We take a dual view of Markov processes ? advocated by Kozen ? as transformers of bounded measurable functions. We redevelop the theory of labelled Markov processes from this view ...
Philippe Chaput, Vincent Danos, Prakash Panangaden...
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...
The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to ...