The Bandit
Bandit Learning with Concurrent Transmissions for Energy-Efficient Flooding in Sensor Networks
... respectively: Action 0 stands for a node staying only in receiving (low-power listening) or sleeping mode, i.e., N = 0; Action N (N = 1, 2, 3) means that a node works normally except setting the maximum transmission ...
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Approximations of the Restless Bandit Problem
... multi-armed bandit problem with strongly dependent pay-offs at its full generality is beyond the scope of this paper, we provide a complementary example for this ...the bandit arms are governed by ...
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Bandit learning in concave N player games
... Our contributions. In this paper, we drop all feedback assumptions and we focus on the bandit framework where the only information at the players’ disposal is the payoffs they receive at each stage. As we ...
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Using Confidence Bounds for Exploitation-Exploration Trade-offs
... The exploitation-exploration trade-off in the associative reinforcement learning model is more subtle than for the bandit problem. Observing the feature vectors the learning algorithm might either go with the ...
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On Multilabel Classification and Ranking with Bandit Feedback
... the bandit setting than the logistic model (“Log Loss”), while the performance of the two models is very similar in the full information ...the bandit algorithm has an even better performance than the full ...
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Profile-Based Bandit with Unknown Profiles
... In this section we propose to apply our SampLinUCB algorithm to the task of dynamic data capture from Twitter introduced in (Gisselbrecht et al., 2015). According to a given information need, the aim is to collect ...
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Uncertainties Related To Structural Model Outputs As A Function Of The Engineering Demand Parameter And Of The Computational Method
... the BANDIT specimen, see Vassaux et ...of BANDIT specimen, additional dissipation has been included in the model by considering the Rayleigh damping ...
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On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models
... close and SGLRT mostly coincides with Elimination, but on the bandit model 0.2 − 0.1 (left) the practical gain of the use of a more sophisticated stopping strategy is well illustrated. Besides, our experiments ...
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Regret Bounds and Minimax Policies under Partial Monitoring
... the bandit game where the forecaster only observes the reward of the arm he has ...efficient bandit game (Gy¨orgy and Ottucs´ak, 2006), in which the only observed rewards are the ones obtained and asked by ...
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Kernel Estimation and Model Combination in A Bandit Problem with Covariates
... In this section, we use the Yahoo! Front Page Today Module User Click Log data set (Yahoo! Academic Relations, 2011) to evaluate the proposed allocation strategy. The complete data set contains about 46 million web page ...
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On Bandit Organizations and Their (IL)Legitimacy: Concept Development and Illustration
... the bandit organization is roving, prosocial and closely identifies with their audience and vice ...economy. Bandit legitimacy is organized by the perception that it is rectifying an ethical deficit in ...
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Training a Quantum Neural Network to Solve the Contextual Multi Armed Bandit Problem
... In this work, we use four qumodes to construct a quantum neural network. The input to the circuit represents the action in the multi-armed bandit problem, and also the state in the contextual multi-armed ...
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Bandit Structured Prediction for Neural Sequence to Sequence Learning
... Learning. Bandit learning starts with the parameters of the out-of-domain ...The bandit models are expected to improve over the out-of-domain baseline by receiving feedback from the new domain, but at most ...
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A multi-arm bandit neighbourhood search for routing and scheduling problems
... We treat the selection of a local search neighbourhood as a dynamic multi- armed bandit (D-MAB) problem where learning techniques for solving the D-MAB can be used to guide the local search process. We present a ...
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Towards an Improved Strategy for Solving Multi Armed Bandit Problem
... Multi-Armed Bandit (MAB) problem is one of the classical reinforcements learning problems that describe the friction between the agent’s exploration and exploitation ...
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Counterfactual Learning from Bandit Feedback under Deterministic Logging : A Case Study in Statistical Machine Translation
... of bandit learning (Bubeck and Cesa- Bianchi, 2012) or reinforcement learning (RL) (Sutton and Barto, ...a bandit), and receives a re- ward, which is used to update the ...
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The consequences of behavioural bias: Bandit problems and product liability law
... contexts, bandit problems and the case o f legal decision ...to bandit problems, the focus o f interest is to examine the role o f risk aversion and loss aversion, which are both excluded from the standard ...
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A multi-armed bandit approach for exploring partially observed networks
... Active search on graphs (Wang et al. 2013; Bilgic et al. 2010) is another related problem with the objective of finding as much target nodes as possible possessing a given prop- erty. Most of the previous work relating ...
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LIMSI Submission for WMT’17 Shared Task on Bandit Learning
... The first Bandit Learning for Machine Translation shared task (Sokolov et al., 2017) aims at adapting a ‘seed’ MT system trained on out-domain corpora to a new domain considering only a ‘weak’ signal, namely a ...
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BinaryBandit:An Efficient Julia Package for Optimization and Evaluation of the Finite Horizon Bandit Problem with Binary Responses
... multi-armed bandit problem for design of sequential experiments have been studied in several disciplines for almost a century, but the performance evaluation of proposed designs or finding a Bayes-optimal design ...
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