Adversarial Bandits

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4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

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Online Conformal Prediction with Adversarial Semi-bandit Feedback via Regret Minimization
Apr 20, 2026Junyoung Yang, Kyungmin Kim, Sangdon ParkUncertainty Quantification Conformal Prediction
GSAI POSTECH · GSAI & CSE POSTECH
Best of both worlds: Stochastic & adversarial best-arm identification
Apr 16, 2026Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon +2Multi-Armed Bandits Best-Arm Identification
Adobe Research, USA · University of California, Berkeley, USA · Queensland University of Technology - ACEMS, Australia +2
Self-Concordant Perturbations for Linear Bandits
Oct 28, 2025Lucas Lévy, Jean-Lou Valeau, Arya Akhavan +1Multi-Armed Bandits Exploration-Exploitation Tradeoff
Ecole Polytechnique and University of Oxford · ENSAE Paris and University of Oxford · University of Oxford and Ecole Polytechnique +1
Best-of-Both Worlds for linear contextual bandits with paid observations
Oct 8, 2025Nathan Boyer, Dorian Baudry, Patrick RebeschiniMulti-Armed Bandits Follow-the-Regularized-Leader
ENS Paris-Saclay, France · Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, 38000 Grenoble, France · Department of Statistics, University of Oxford

Apr 20, 2026, cs.LGJ/K move · Enter open · S save

Online Conformal Prediction with Adversarial Semi-bandit Feedback via Regret Minimization

Junyoung Yang, Kyungmin Kim, Sangdon Park

GSAI POSTECH · GSAI & CSE POSTECH

Uncertainty Quantification Conformal Prediction Adversarial Bandits

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Uncertainty quantification is crucial in safety-critical systems, where decisions must be made under uncertainty. In particular, we consider the problem of online uncertainty quantification, where data points arrive sequentially. Online conformal prediction is a principled online uncertainty quantification method that dynamically constructs a prediction set at each time step. While existing methods for online conformal prediction provide long-run coverage guarantees without any distributional assumptions, they typically assume a full feedback setting in which the true label is always observed. In this paper, we propose a novel learning method for online conformal prediction with partial feedback from an adaptive adversary-a more challenging setup where the true label is revealed only when it lies inside the constructed prediction set. Specifically, we formulate online conformal prediction as an adversarial bandit problem by treating each candidate prediction set as an arm. Building on an existing algorithm for adversarial bandits, our method achieves a long-run coverage guarantee by explicitly establishing its connection to the regret of the learner. Finally, we empirically demonstrate the effectiveness of our method in both independent and identically distributed (i.i.d.) and non-i.i.d. settings, showing that it successfully controls the miscoverage rate while maintaining a reasonable size of the prediction set.

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Latest papers 34

Online Conformal Prediction with Adversarial Semi-bandit Feedback via Regret Minimization

Best of both worlds: Stochastic & adversarial best-arm identification

Self-Concordant Perturbations for Linear Bandits

Best-of-Both Worlds for linear contextual bandits with paid observations