Bandit problems
Batched Bandit Problems
... trials. Bandit problems capture a fun- damental exploration-exploitation dilemma that has made the framework popular for studying problems not only in clinical trials but also in economics, finance, ...
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On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems
... A challenging variant of the MABP is the non-stationary bandit problem where the gambler must de- cide which arm to play while facing the possibility of a changing environment. In this paper, we consider the ...
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Conservation laws, extended polymatroids and multi-armed bandit problems : a unified approach to indexable systems
... show that linear optimization problems over extended polymatroids can be solved by an adaptive greedy algorithm. Most importantly, we show that this optimization problem has an indexabil[r] ...
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Conservation Laws, Extended Polymatroids and Multi-Armed Bandit Problems; A unified Approach to Indexabel Systems
... sufficient condition for idexable systems to be decomposable, (3) a new linear programming proof of the decomposability property of Gittins indices in multi-armed band[r] ...
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On Multi-Armed Bandit Designs for Dose-Finding Trials
... a bandit model mostly fall in two categories: frequentist algo- rithms, based on upper-confidence bounds (UCB) for the unknown means of the arms (popularized by Kate- hakis and Robbins ( 1995 ); Auer et ...simple ...
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Pépite | Allocation séquentielle de ressources dans le modèle de bandit linéaire
... linear bandit setting ...linear bandit problems, it might be the case that “discarded arms” (that is, the arms that are sub-optimal with high probability), should continue to be ...
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A Neural Networks Committee for the Contextual Bandit Problem
... find the leaves which provide the highest rewards [13]. The combinatorial aspect of these approaches limits their use to small context size. Seldin et al [14] modelize the contexts by state sets, which are associated ...
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Contextual Bandit for Active Learning: Active Thompson Sampling
... contextual bandit has been used in different domains such as recommender system (RS) and information ...multi-armed bandit problems and it is a randomized algorithm based on Bayesian ...contextual ...
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The Non-stationary Stochastic Multi-armed Bandit Problem
... multi-armed bandit problem that generalize the stationary stochastic, piecewise- stationary and adversarial bandit ...identification problems involv- ing non-stationary distributions, while achieving ...
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Bandit algorithms for recommender system optimization
... for some β > 0. Many-armed bandits Models in many-armed bandit problems are more varied. Teytaud, Gelly, and Sebag [ 136 ] provided an anytime algorithm when the number of arms is large compar- atively ...
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Sur la notion d'optimalité dans les problèmes de bandit stochastique
... stochastic bandit models with exponential families of distributions or with distribution only assumed to be supported by the unit interval, that are simultaneously asymptoti- cally optimal (in the sense of Lai and ...
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Maximin Action Identification: A New Bandit Framework for Games
... Wouter M. Koolen WMKOOLEN @ CWI . NL Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands Abstract We study an original problem of pure exploration in a strategic bandit model ...
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CCN Interest Forwarding Strategy as Multi-Armed Bandit Model with Delays
... Second, we have also contributed to the theory of the multi-armed bandit problem with delayed information. This is an important and challenging topic with few existing results. We have provided finite-time ...
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R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systems
... In [16], the authors propose to solve bandit problem in dynamic environment by combining the UCB with the ǫ-greedy strategy and they dynamically update the ǫ exploration value. At each iteration, they run a ...
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Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence
... finite bandit model an arm with mean larger than µ [m] − (with µ [m] the arm with m-th largest mean) is extended to the infinite case, in which the aim is to find an (α, )-optimal ...
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A Bayesian bandit approach to personalized online coupon recommendations
... This paper resolves this problem by using a multi-armed bandit approach to balance the exploration (learning customers' preference for coupons) with exploitation (ma[r] ...
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Entre crash de l'État magique et boom de l'État bandit: le Venezuela dans le labyrinthe autoritaire
... Cet état de fait renvoie tout à la fois à des formes – bien réelles – de surveillance ; à la méfiance populaire vis-à-vis des tenants présumés de l’Ancien régime ; au refus proclamé d[r] ...
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Learning the distribution with largest mean: two bandit frameworks
... still not trivial. This would in particular permit to discriminate from a theoretical perspective between all the bandit algorithms that are now known to be asymptotically optimal, but for which significant ...
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On the Complexity of Best Arm Identification in Multi-Armed Bandit Models
... multi-armed bandit model is a simple abstraction that has proven useful in many different contexts in statistics and machine ...for bandit models (Lemma ...
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Node-based optimization of LoRa transmissions with Multi-Armed Bandit algorithms
... Multi-Armed Bandit algorithms Raouf Kerkouche ‹ , Reda Alami ˛ , Raphaël Féraud ˛ , Nadège Varsier ˛ , Patrick Maillé ‚ Abstract— The use of Low Power Wide Area Networks (LPWANs) is growing due to their advantages ...
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