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Wrappers for Feature Subset Selection
 AIJ SPECIAL ISSUE ON RELEVANCE
, 1997
"... In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a ..."
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Cited by 1522 (3 self)
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In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact. We explore the relation between optimal feature subset selection and relevance. Our wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain. We study the strengths and weaknesses of the wrapper approach andshow a series of improved designs. We compare the wrapper approach to induction without feature subset selection and to Relief, a filter approach to feature subset selection. Significant improvement in accuracy is achieved for some datasets for the two families of induction algorithms used: decision trees and NaiveBayes.
A ContextualBandit Approach to Personalized News Article Recommendation
"... Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamic ..."
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Cited by 170 (16 self)
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contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its articleselection strategy based on userclick feedback to maximize total user clicks
Contextual Bandits with Similarity Information
 24TH ANNUAL CONFERENCE ON LEARNING THEORY
, 2011
"... In a multiarmed bandit (MAB) problem, an online algorithm makes a sequence of choices. In each round it chooses from a timeinvariant set of alternatives and receives the payoff associated with this alternative. While the case of small strategy sets is by now wellunderstood, a lot of recent work ha ..."
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Cited by 53 (8 self)
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of contextual bandits, a natural extension of the basic MAB problem where before each round an algorithm is given the context – a hint about the payoffs in this round. Contextual bandits are directly motivated by placing advertisements on webpages, one of the crucial problems in sponsored search. A particularly
Extreme bandits
"... In many areas of medicine, security, and life sciences, we want to allocate limited resources to different sources in order to detect extreme values. In this paper, we study an efficient way to allocate these resources sequentially under limited feedback. While sequential design of experiments is w ..."
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Cited by 3 (0 self)
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is well studied in bandit theory, the most commonly optimized property is the regret with respect to the maximum mean reward. However, in other problems such as network intrusion detection, we are interested in detecting the most extreme value output by the sources. Therefore, in our work we study
Contextual MultiArmed Bandits
"... We study contextual multiarmed bandit problems where the context comes from a metric space and the payoff satisfies a Lipschitz condition with respect to the metric. Abstractly, a contextual multiarmed bandit problem models a situation where, in a sequence of independent trials, an online algorith ..."
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Cited by 15 (0 self)
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We study contextual multiarmed bandit problems where the context comes from a metric space and the payoff satisfies a Lipschitz condition with respect to the metric. Abstractly, a contextual multiarmed bandit problem models a situation where, in a sequence of independent trials, an online
The EpochGreedy Algorithm for Contextual Multiarmed Bandits
"... We present EpochGreedy, an algorithm for contextual multiarmed bandits (also known as bandits with side information). EpochGreedy has the following properties: 1. No knowledge of a time horizon T is necessary. 2. The regret incurred by EpochGreedy is controlled by a sample complexity bound for a ..."
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Cited by 78 (9 self)
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We present EpochGreedy, an algorithm for contextual multiarmed bandits (also known as bandits with side information). EpochGreedy has the following properties: 1. No knowledge of a time horizon T is necessary. 2. The regret incurred by EpochGreedy is controlled by a sample complexity bound
Contextual Bandits with Linear Payoff Functions
"... In this paper we study the contextual bandit problem (also known as the multiarmed bandit problem with expert advice) for linear payoff functions. For T rounds, K actions, and d(√ dimensional feature vectors, we prove an O T d ln 3) (KT ln(T)/δ) regret bound that holds with probability 1 − δ for th ..."
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Cited by 37 (4 self)
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In this paper we study the contextual bandit problem (also known as the multiarmed bandit problem with expert advice) for linear payoff functions. For T rounds, K actions, and d(√ dimensional feature vectors, we prove an O T d ln 3) (KT ln(T)/δ) regret bound that holds with probability 1 − δ
Aggressive Learning for Contextual Bandits
, 2011
"... The contextual bandit setting consists of the following basic loop repeated indefinitely: 1. The world presents context information as features x. 2. The learning algorithm chooses an action a. 3. The world presents a reward r for the action. ..."
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The contextual bandit setting consists of the following basic loop repeated indefinitely: 1. The world presents context information as features x. 2. The learning algorithm chooses an action a. 3. The world presents a reward r for the action.
Results 1  10
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