4 accepted papers
Correlation clustering is a fundamental optimization problem at the intersection of machine learning and theoretical computer science. Motivated by applications to big data processing, recent years have witnessed a flurry of results on this problem in the streaming model. In this model, the algori…
Motivated by applications to process massive datasets, we study streaming algorithms for pure exploration in Stochastic Multi-Armed Bandits (MABs). This problem was first formulated by Assadi and Wang [STOC 2020] as follows: A collection of $n$ arms with unknown rewards are arriving one by one in a…
Maximum weight matching is one of the most fundamental combinatorial optimization problems with a wide range of applications in data mining and bioinformatics. Developing distributed weighted matching algorithms has been challenging due to the sequential nature of efficient algorithms for this probl…
We study a secretary problem which captures the task of ranking in online settings. We term this problem the secretary ranking problem: elements from an ordered set arrive in random order and instead of picking the maximum element, the algorithm is asked to assign a rank, or position, to each of the…