5 papers · 1 filter
The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
Maximilian Graf, Victor Thuot
We study multiple change point localization under bandit feedback. An unknown piecewise-constant function on a compact interval can be queried sequentially at adaptively chosen inp…
The Sampling Complexity of Condorcet Winner Identification in Dueling Bandits
El Mehdi Saad, Victor Thuot, Nicolas Verzelen
We study best-arm identification in stochastic dueling bandits under the sole assumption that a Condorcet winner exists, i.e., an arm that wins each noisy pairwise comparison with…
Nonparametric Kernel Clustering with Bandit Feedback
Victor Thuot, Sebastian Vogt, Debarghya Ghoshdastidar +1
Clustering with bandit feedback refers to the problem of partitioning a set of items, where the clustering algorithm can sequentially query the items to receive noisy observations.…
Clustering Items through Bandit Feedback: Finding the Right Feature out of Many
Maximilian Graf, Victor Thuot, Nicolas Verzelen
We study the problem of clustering a set of items based on bandit feedback. Each of the items is characterized by a feature vector, with a possibly large dimension . The ite…
Active clustering with bandit feedback
Victor Thuot, Alexandra Carpentier, Christophe Giraud +1
We investigate the Active Clustering Problem (ACP). A learner interacts with an -armed stochastic bandit with -dimensional subGaussian feedback. There exists a hidden partiti…