arXiv:2510. 15824v2 Announce Type: replace-cross Abstract: This article considers an online version of conformal inference, called adaptive conformal inference [ACI] and introduced by Gibbs and Cand\`es (2021): prediction sets are issued sequentially, after observing features and before the outcomes are revealed.
arXiv:2607. 26577v1 Announce Type: new Abstract: Adaptive conformal inference (ACI) of Gibbs and Cand{\`e}s and its variants are the standard approach to online conformal prediction under distribution shift, but they suffer from three fundamental limitations.
arXiv:2605. 14953v2 Announce Type: replace Abstract: We address the problem of conformal selection, where an agent must select a minimal subset of options to ensure that at least one ``success'' is identified with a pre-specified target probability $\phi$.
arXiv:2508.10336v3 Announce Type: replace-cross Abstract: In a supervised online setting, quantifying uncertainty has been proposed in the seminal work of Gibbs and Cand\`es (2021). For any given poi...
arXiv:2306. 02704v2 Announce Type: replace-cross Abstract: We introduce \emph{Calibrated Stackelberg Games (CSGs)}, a generalization of the standard Stackelberg Games (SGs) framework.
The paper addresses the failure of online conformal prediction when predictions influence actions that determine which outcomes are used for calibration. It introduces Propensity-Weighted Online Conformal Prediction (PW‑OCP), an inverse‑propensity‑weighted recursion that debiases calibration, and a doubly robust variant (DR‑OCP) that further reduces bias. Experiments on synthetic decision tasks, open bandit data, and financial rebalancing demonstrate that PW‑OCP and DR‑OCP improve counterfactual coverage and downstream regret while preserving prediction‑set sharpness.
arXiv:2606. 06486v1 Announce Type: new Abstract: In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play.
arXiv:2607. 02206v1 Announce Type: cross Abstract: Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making.
arXiv:2606. 27315v1 Announce Type: new Abstract: Gradient equilibrium (GEQ) is a recently introduced online optimization framework that generalizes first-order stationarity from offline optimization and abstracts problems like online conformal prediction.
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
arXiv:2512. 09850v2 Announce Type: replace Abstract: We introduce Conformal Bandits, a novel framework integrating Conformal Prediction (CP) into bandit problems, a classic paradigm for sequential decision-making under uncertainty.
arXiv:2602. 01903v2 Announce Type: replace Abstract: This work studies online episodic tabular Markov decision processes (MDPs) with known transitions and develops best-of-both-worlds algorithms that achieve refined data-dependent regret bounds in the adversarial regime and variance-dependent regret bounds in the stochastic regime.
arXiv:2504.09192v5 Announce Type: replace Abstract: The primary goal of my Ph.D. study is to develop provably efficient and practical algorithms for data-driven sequential decision-making under uncer...