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Found 18 papers, 1 papers with code
Symphony of experts: orchestration with adversarial insights in reinforcement learning
Structured reinforcement learning leverages policies with advantageous properties to reach better performance, particularly in scenarios where exploration poses challenges.
Parameter-free projected gradient descent
We consider the problem of minimizing a convex function over a closed convex set, with Projected Gradient Descent (PGD).
On Best-Arm Identification with a Fixed Budget in Non-Parametric Multi-Armed Bandits
All these new upper and lower bounds generalize existing bounds based, e. g., on gaps between distributions.
Contextual Bandits with Knapsacks for a Conversion Model
At each round, given the stochastic i. i. d.\ context $\mathbf{x}_t$ and the arm picked $a_t$ (corresponding, e. g., to a discount level), a customer conversion may be obtained, in which case a reward $r(a,\mathbf{x}_t)$ is gained and vector costs $c(a_t,\mathbf{x}_t)$ are suffered (corresponding, e. g., to losses of earnings).
A Unified Approach to Fair Online Learning via Blackwell Approachability
We provide a setting and a general approach to fair online learning with stochastic sensitive and non-sensitive contexts.
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 $[m, M]$.
Hierarchical robust aggregation of sales forecasts at aggregated levels in e-commerce, based on exponential smoothing and Holt's linear trend method
We revisit the interest of classical statistical techniques for sales forecasting like exponential smoothing and extensions thereof (as Holt's linear trend method).
Target Tracking for Contextual Bandits: Application to Demand Side Management
We propose a contextual-bandit approach for demand side management by offering price incentives.
Sequential model aggregation for production forecasting
An extension of the deterministic aggregation approach is thus proposed in this paper to provide such multi-step-ahead forecasts.
Uniform regret bounds over $R^d$ for the sequential linear regression problem with the square loss
In the case of sequentially revealed features, we also derive an asymptotic regret bound of $d B^2 \ln T$ for any individual sequence of features and bounded observations.
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.
Explore First, Exploit Next: The True Shape of Regret in Bandit Problems
We revisit lower bounds on the regret in the case of multi-armed bandit problems.
Approachability in unknown games: Online learning meets multi-objective optimization
We show that it is impossible, in general, to approach the best target set in hindsight and propose achievable though ambitious alternative goals.
A Second-order Bound with Excess Losses
We study online aggregation of the predictions of experts, and first show new second-order regret bounds in the standard setting, which are obtained via a version of the Prod algorithm (and also a version of the polynomially weighted average algorithm) with multiple learning rates.
A Primal Condition for Approachability with Partial Monitoring
In this paper we provide primal conditions on a convex set to be approachable with partial monitoring.
Mirror Descent Meets Fixed Share (and feels no regret)
Mirror descent with an entropic regularizer is known to achieve shifting regret bounds that are logarithmic in the dimension.
Online Optimization in X-Armed Bandits
We consider a generalization of stochastic bandit problems where the set of arms, X, is allowed to be a generic topological space.
