In a 2-dimensional setting, how many different classes of partially distinguishable landmarks are needed to ensure that a robot can always see a landmark without simultaneously seeing two of the same class?
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.
