Since its invention by Robbins and Monro in 1951, the stochastic approximation (SA) algorithm has been a widely used tool for finding solutions of equations, or minimizing functions, with noisy measurements.
Agent-based simulators are a popular epidemiological modelling tool to study the impact of various non-pharmaceutical interventions in managing an evolving pandemic.
In the first part of this talk we will discuss the notion of rank of decision trees, which is essentially the largest depth of a complete subtree embedded in the initial tree.
In this talk we will describe some of our recent work giving new upper and lower bounds on the approximability of constraint satisfaction problems (CSPs) in the streaming and sketching settings.
A game with *perfect information* is a game in which no information is hidden from the players. All players know the rules and the state of the game at all times.