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Spectral Thompson Sampling
, 2014
"... Thompson Sampling (TS) has surged a lot of interest due to its good empirical performance, in particular in the computational advertising. Though successful, the tools for its performance analysis appeared only recently. In this paper, we describe and analyze SpectralTS algorithm for a bandit prob ..."
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Cited by 3 (2 self)
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Thompson Sampling (TS) has surged a lot of interest due to its good empirical performance, in particular in the computational advertising. Though successful, the tools for its performance analysis appeared only recently. In this paper, we describe and analyze SpectralTS algorithm for a bandit problem, where the payoffs of the choices are smooth given an underlying graph. In this setting, each choice is a node of a graph and the expected payoffs of the neighboring nodes are assumed to be similar. Although the setting has application both in recommender systems and advertising, the traditional algorithms would scale poorly with the number of choices. For that purpose we consider an effective dimension d, which is small in realworld graphs. We deliver the analysis showing that the regret of SpectralTS scales as d T lnN with high probability, where T is the time horizon and N is the number of choices. Since a d T lnN regret is comparable to the known results, SpectralTS offers a computationally more efficient alternative. We also show that our algorithm is competitive on both synthetic and realworld data.
EFFICIENT DETECTION AND LOCALIZATION ON GRAPH STRUCTURED DATA
"... The problem of efficiently identifying regions of interest arises in the context of surveillance, monitoring and exploration of a large area or network involving social, sensor, communication network data. We formulate these problems in terms of locating optimum values of signals on graphs. In this ..."
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The problem of efficiently identifying regions of interest arises in the context of surveillance, monitoring and exploration of a large area or network involving social, sensor, communication network data. We formulate these problems in terms of locating optimum values of signals on graphs. In this perspective we associate features with nodes/edges of a graph where the maxima/minima of these features correspond to interest points. We develop an algorithm that adaptively probes local subcollection of nodes (local regions) on the graph and sequentially refines the search space from noisy averaged returns from each probed region. The size of the region determines the cost of the probe with larger regions corresponding to lower cost. Our goal is to minimize regret after T rounds with minimal budget/cost. Under suitable smoothness conditions on the signal we show that after T rounds the cumulative regret scales optimally as O( T) with significant cost gain over other stateofart techniques. 1.
Intelligent Approaches for Communication Denial
, 2015
"... Spectrum supremacy is a vital part of security in the modern era. In the past 50 years, a great deal of work has been devoted to designing defenses against attacks from malicious nodes (e.g., antijamming), while significantly less work has been devoted to the equally important task of designing eff ..."
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Spectrum supremacy is a vital part of security in the modern era. In the past 50 years, a great deal of work has been devoted to designing defenses against attacks from malicious nodes (e.g., antijamming), while significantly less work has been devoted to the equally important task of designing effective strategies for denying communication between enemy nodes/radios within an area (e.g., jamming). Such denial techniques are especially useful in military applications and intrusion detection systems where untrusted communication must be stopped. In this dissertation, we study these offensive attack procedures, collectively termed as communication denial. The communication denial strategies studied in this dissertation are not only useful in undermining the communication between enemy nodes, but also help in analyzing the vulnerabilities of existing systems. A majority of the works which address communication denial assume that knowledge about the enemy nodes is available a priori. However, recent advances in communication systems creates the potential for dynamic environmental conditions where it is difficult and most likely not even possible to obtain a priori information regarding the environment and the nodes that are present in it. Therefore, it is necessary to have cognitive capabilities that enable the attacker to learn
Efficient Thompson Sampling for Online MatrixFactorization Recommendation
"... Matrix factorization (MF) collaborative filtering is an effective and widely used method in recommendation systems. However, the problem of finding an optimal tradeoff between exploration and exploitation (otherwise known as the bandit problem), a crucial problem in collaborative filtering from col ..."
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Matrix factorization (MF) collaborative filtering is an effective and widely used method in recommendation systems. However, the problem of finding an optimal tradeoff between exploration and exploitation (otherwise known as the bandit problem), a crucial problem in collaborative filtering from coldstart, has not been previously addressed. In this paper, we present a novel algorithm for online MF recommendation that automatically combines finding the most relevant items with exploring new or lessrecommended items. Our approach, called Particle Thompson sampling for MF (PTS), is based on the general Thompson sampling framework, but augmented with a novel efficient online Bayesian probabilistic matrix factorization method based on the RaoBlackwellized particle filter. Extensive experiments in collaborative filtering using several realworld datasets demonstrate that PTS significantly outperforms the current stateofthearts. 1
Spectral Bandits for Smooth Graph Functions with Applications in Recommender Systems
, 2014
"... Smooth functions on graphs have wide applications in manifold and semisupervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as contentbased recommenda ..."
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Smooth functions on graphs have wide applications in manifold and semisupervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as contentbased recommendation. In this problem, each recommended item is a node and its expected rating is similar to its neighbors. The goal is to recommend items that have high expected ratings. We aim for the algorithms where the cumulative regret would not scale poorly with the number of nodes. In particular, we introduce the notion of an effective dimension, which is small in realworld graphs, and propose two algorithms for solving our problem that scale linearly in this dimension. Our experiments on realworld content recommendation problem show that a good estimator of user preferences for thousands of items can be learned from just tens nodes evaluations.
New England
"... Smooth functions on graphs have wide applications in manifold and semisupervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as contentbased recommen ..."
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Smooth functions on graphs have wide applications in manifold and semisupervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as contentbased recommendation. In this problem, each recommended item is a node and its expected rating is similar to its neighbors. The goal is to recommend items that have high expected ratings. We aim for the algorithms where the cumulative regret would not scale poorly with the number of nodes. In particular, we introduce the notion of an effective dimension, which is small in realworld graphs, and propose two algorithms for solving our problem that scale linearly in this dimension. Our experiments on realworld content recommendation problem show that a good estimator of user preferences for thousands of items can be learned from just tens nodes evaluations. 1
SequeL team
"... Thompson Sampling (TS) has surged a lot of interest due to its good empirical performance, in particular in the computational advertising. Though successful, the tools for its performance analysis appeared only recently. In this paper, we describe and analyze SpectralTS algorithm for a bandit prob ..."
Abstract
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Thompson Sampling (TS) has surged a lot of interest due to its good empirical performance, in particular in the computational advertising. Though successful, the tools for its performance analysis appeared only recently. In this paper, we describe and analyze SpectralTS algorithm for a bandit problem, where the payoffs of the choices are smooth given an underlying graph. In this setting, each choice is a node of a graph and the expected payoffs of the neighboring nodes are assumed to be similar. Although the setting has application both in recommender systems and advertising, the traditional algorithms would scale poorly with the number of choices. For that purpose we consider an effective dimension d, which is small in realworld graphs. We deliver the analysis showing that the regret of SpectralTS scales as d T lnN with high probability, where T is the time horizon and N is the number of choices. Since a d T lnN regret is comparable to the known results, SpectralTS offers a computationally more efficient alternative. We also show that our algorithm is competitive on both synthetic and realworld data. 1
Online Clustering of Bandits Claudio Gentile
"... We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of explorationexploitation (“bandit”) strategies. We provide a sharp regret analysis of this algorithm in a standard stochastic noise setting, demonstrate its scalability properties, and prove its effe ..."
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We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of explorationexploitation (“bandit”) strategies. We provide a sharp regret analysis of this algorithm in a standard stochastic noise setting, demonstrate its scalability properties, and prove its effectiveness on a number of artificial and realworld datasets. Our experiments show a significant increase in prediction performance over stateoftheart methods for bandit problems. 1.