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Algorithmic aspects of graph-indexed random walks
We study three problems regarding the so called graph-indexed random wal...
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Random Walk Fundamental Tensor and its Applications to Network Analysis
We first present a comprehensive review of various random walk metrics u...
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Lévy Flights of the Collective Imagination
We present a structured random-walk model that captures key aspects of h...
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Algorithmic aspects of M-Lipschitz mappings of graphs
M-Lipschitz mappings of graphs (or equivalently graph-indexed random wal...
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A Random Walk Approach to First-Order Stochastic Convex Optimization
Online minimization of an unknown convex function over a convex and comp...
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A Divergence Proof for Latuszynski's Counter-Example Approaching Infinity with Probability "Near" One
This note is a technical supplement to the following paper: latuszynski2...
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"Drunk Man" Saves Our Lives: Route Planning by a Biased Random Walk Mode
Based on the hurricane striking Puerto Rico in 2017, we developed a tran...
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Random Walk Bandits
Bandit learning problems find important applications ranging from medical trials to online advertisement. In this paper, we study a novel bandit learning problem motivated by recommender systems. The goal is to recommend items so that users are likely to continue browsing. Our model views a user's browsing record as a random walk over a graph of webpages. This random walk ends (hits an absorbing node) when the user exits the website. Our model introduces a novel learning problem that calls for new technical insights on learning with graph random walk feedback. In particular, the performance and complexity depend on the structure of the decision space (represented by graphs). Our paper provides a comprehensive understanding of this new problem. We provide bandit learning algorithms for this problem with provable performance guarantees, and provide matching lower bounds.
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