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Probing and Stopping-Time Algorithms Combinatorial optimization captures many natural problems such as matching, load balancing, social welfare, network design, clustering, and submodular optimization. Classically, these problems have been studied in the full-information setting, i.
In this thesis we focus on combinatorial problems in an uncertain environment where we only have partial knowledge about the input. In particular, we study models where the input is revealed to us element-by-element and we have to make irrevocable decisions.
Depending on whether we can control the revelation order of these elements, we separate our models and algorithms into two classes. In these models we have stochastic knowledge about the input, but the uncertainty of an element realizes only after we probe it.
We can choose the order and the set of elements to probe; however, we do not wish to probe all of them as either probing incurs a price the price of information model or there are probing constraints the constrained stochastic probing model.
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In these models the input is revealed element- by-element in an order that we cannot control. These models are inspired from work in the field of Stopping Theory. In particular, we consider combinatorial problems when either we have stochastic knowledge about the input but the revelation order is chosen by an adversary the Prophet Inequality model or when we have no prior knowledge about the input but the revelation order is chosen uniformly at random the Secretary model.GAUTAM CHETAN KAMATH ADDRESS 32 Vassar Street Room G Cambridge, MA CONTACT S.M.
Thesis, Gautam Kamath, Robert Kleinberg Proceedings of the 44th ACM Symposium on Theory of Computing (STOC ) TALKS Principled Tools for Modern Statistical Data Science Boston University Computer Science Seminar, February Thesis: Approximation algorithms for disjoint paths problems He is the older brother of fellow Cornell computer scientist Robert Kleinberg.
Career. Since Kleinberg has been a professor in the Department of Computer Science at Cornell. In this thesis we focus on combinatorial problems in an uncertain environment where we only have partial knowledge about the input. In particular, we study models where uncertainty in the input is revealed to us element-by-element, and we have to make immediate and irrevocable decisions.
(under Prof. Robert Kleinberg), Cornell University, Summer [T1] Noisy Compressed Sensing and Sparse Channel Estimation, srmvision.com Thesis (under Prof. Masoud Babaie-Zadeh), Sharif University of Technology, Summer Computer Science Field Description. The Field of Computer Science is intended for students who are primarily interested in the general aspects of computational processes, both theoretical and practical.
ROBERT KLEINBERG, COMPUTER SCIENCE.
62 What information do we actually need in order to make the best decisions, and what way to producing my doctoral thesis. The same techniques I employed in answering that question have fed into other computer science problems in .