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Found 7 papers, 1 papers with code
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness
case, our bounds match the rates one would expect from results in stochastic acceleration, and in the fully adversarial case they gracefully deteriorate to match the minimax regret.
Scale-free Unconstrained Online Learning for Curved Losses
We follow up on this observation by showing that there is in fact never a price to pay for adaptivity if we specialise to any of the other common supervised online learning losses: our results cover log loss, (linear and non-parametric) logistic regression, square loss prediction, and (linear and non-parametric) least-squares regression.
Distributed Online Learning for Joint Regret with Communication Constraints
Each round, an adversary first activates one of the agents to issue a prediction and provides a corresponding gradient, and then the agents are allowed to send a $b$-bit message to their neighbors in the graph.
Diversity-Preserving K-Armed Bandits, Revisited
We consider the bandit-based framework for diversity-preserving recommendations introduced by Celis et al. (2019), who approached it in the case of a polytope mainly by a reduction to the setting of linear bandits.
Adaptation to the Range in $K$-Armed Bandits
We consider stochastic bandit problems with $K$ arms, each associated with a bounded distribution supported on the range $[m, M]$.
Polynomial Cost of Adaptation for X -Armed Bandits
In the context of stochastic continuum-armed bandits, we present an algorithm that adapts to the unknown smoothness of the objective function.
KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints
We were able to obtain this non-parametric bi-optimality result while working hard to streamline the proofs (of previously known regret bounds and thus of the new analyses carried out); a second merit of the present contribution is therefore to provide a review of proofs of classical regret bounds for index-based strategies for $K$-armed stochastic bandits.
