Multi-armed bandits – on range searching and on slowly-varying non-stationarity
Material type: BookLanguage: en. Publication details: Bengaluru IISc 2022Description: 68p. e-Thesis col. ill. ; 29.1 cm * 20.5 cm 1.367MbDissertation: MTech (Res); 2022; Computer science and automationSubject(s): DDC classification:- 600 RAM
Item type | Current library | Call number | Status | Date due | Barcode |
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E-BOOKS | JRD Tata Memorial Library | 600 RAM (Browse shelf(Opens below)) | Available | ET00058 |
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MTech (Res); 2022; Computer science and automation
Multi-Armed Bandits (MAB) is a popular framework for modelling sequential decision-making problems under uncertainty. This thesis is a compilation of two independent works on MABs. 1. In the first work, we study a multi-armed bandit (MAB) version of the range-searching problem. In its basic form, range searching considers as input a set of points (on the real line) and a collection of (real) intervals. Here, with each specified point, we have an associated weight, and the problem objective is to find a maximum-weight point within every given interval. The current work addresses range searching with stochastic weights: each point corresponds to an arm (that admits sample access), and the point’s weight is the (unknown) mean of the underlying distribution. In this MAB setup, we develop sample-efficient algorithms that find, with high probability, near-optimal arms within the given intervals, i.e., we obtain PAC (probably approximately correct) guarantees. We also provide an algorithm for a generalization wherein the weight of each point is a multi-dimensional vector.
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