93 citations · 117 across the 3 of their papers we have counts for
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Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits
Alexandra Carpentier, Alessandro Lazaric, Mohammad Ghavamzadeh +3
In this paper, we study the problem of estimating uniformly well the mean values of several distributions given a finite budget of samples. If the variance of the distributions wer…
Risk-Aversion in Multi-armed Bandits
Amir Sani, Alessandro Lazaric, Rémi Munos
Stochastic multi-armed bandits solve the Exploration-Exploitation dilemma and ultimately maximize the expected reward. Nonetheless, in many practical problems, maximizing the expec…
A Dantzig Selector Approach to Temporal Difference Learning
Matthieu Geist, Bruno Scherrer, Alessandro Lazaric +1
LSTD is a popular algorithm for value function approximation. Whenever the number of features is larger than the number of samples, it must be paired with some form of regularizati…