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New OCO Framework Improves Regret with Noisy Probes

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework for Online Convex Optimization (OCO) that can improve worst-case regret even with a limited and noisy budget of pairwise probes. The proposed method unifies sublinear best-expert queries and pairwise feedback, showing that a sublinear, noisy probe budget can provably enhance regret in the full feedback OCO regime. The analysis quantifies the benefit of probing through variance reduction and a second-order analysis of Continuous Exponential Weights, yielding tight regret guarantees. AI

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework for Online Convex Optimization.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Simone Di Gregorio, Anupam Gupta, Stefano Leonardi, Matteo Russo ·

    Online Convex Optimization with Sublinear Noisy Probes

    arXiv:2606.14640v1 Announce Type: new Abstract: We study Online Convex Optimization (OCO) over a convex set $K\subseteq \mathbb R^d$, where in each round $t$ the learner selects $x_t\in K$ and then observes a convex loss $f_t:K\to[0,1]$, with the goal of minimizing regret to the …

  2. arXiv cs.LG TIER_1 English(EN) · Matteo Russo ·

    Online Convex Optimization with Sublinear Noisy Probes

    We study Online Convex Optimization (OCO) over a convex set $K\subseteq \mathbb R^d$, where in each round $t$ the learner selects $x_t\in K$ and then observes a convex loss $f_t:K\to[0,1]$, with the goal of minimizing regret to the best fixed decision in hindsight. We introduce a…

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