In this talk we will study the communication complexity of the disjointness function, in which each of two players holds a $k$-subset of a universe of size $n$ and the goal is to determine whether the sets are disjoint.
We study the performance of static solutions for two-stage adjustable robust linear optimization problems with uncertain constraint and objective coefficients and give a tight characterization of the adaptivity gap.
The Johnson-Lindenstrauss lemma asserts that any n-point set in any Euclidean space can be mapped to a Euclidean space of dimension $k= O(\epsilon^{-2} \log n)$ so that all distances are preserved upto a multiplicative factor between $(1 - \epsilo
In machine learning, AdaBoost has been an extremely popular boosting algorithm to improve the performance of ``weak learners". AdaBoost was initially proposed by Schapire and Freund from an algorithmic perspective.
Suppose you want to find the least congested route in an ad hoc network. Each link's rate is unknown and stochastic, and each time you get to see the minimum rate (i.e., bottleneck) along any route you pick.
Networks of chemical reactions have natural underlying combinatorial structure, allowing them to be represented as graphs or digraphs, perhaps with additional vertex or edge colourings/labellings.
