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20132024
most citedTowards Healthy AI: Large Language Models Need Therapists Too

11 citations · 40 across the 15 of their papers we have counts for

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Showing 2014Show all

6 papers · 1 filter

cs.IR20141 cited

Freshness-Aware Thompson Sampling

Djallel Bouneffouf

To follow the dynamicity of the user's content, researchers have recently started to model interactions between users and the Context-Aware Recommender Systems (CARS) as a bandit p…

cs.NE201410 cited

A Neural Networks Committee for the Contextual Bandit Problem

Robin Allesiardo, Raphael Feraud, Djallel Bouneffouf

This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to…

cs.IR20143 cited

Context-Based Information Retrieval in Risky Environment

Djallel Bouneffouf

Context-Based Information Retrieval is recently modelled as an exploration/ exploitation trade-off (exr/exp) problem, where the system has to choose between maximizing its expected…

cs.LG2014

Exponentiated Gradient Exploration for Active Learning

Djallel Bouneffouf

Active learning strategies respond to the costly labelling task in a supervised classification by selecting the most useful unlabelled examples in training a predictive model. Many…

cs.IR20142 cited

R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systems

Djallel Bouneffouf

Mobile Context-Aware Recommender Systems can be naturally modelled as an exploration/exploitation trade-off (exr/exp) problem, where the system has to choose between maximizing its…

cs.IR20142 cited

Étude des dimensions spécifiques du contexte dans un système de filtrage d'informations

Djallel Bouneffouf

In the context of business information systems, e-commerce and access to knowledge, the relevance of the information provided to use is a key fact to the success of information sys…