Exploration-Exploitation Tradeoff

Follow this topic Ask about this topic

Momentum

3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 32

All topics

Taming the Monster Every Context: Complexity Measure and Unified Framework for Offline-Oracle Efficient Contextual Bandits
Feb 10, 2026Hao Qin, Chicheng ZhangExploration-Exploitation Tradeoff Contextual Bandits
The University of Arizona
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

Feb 10, 2026, cs.LGJ/K move · Enter open · S save

Taming the Monster Every Context: Complexity Measure and Unified Framework for Offline-Oracle Efficient Contextual Bandits

Hao Qin, Chicheng Zhang

The University of Arizona

Exploration-Exploitation Tradeoff Contextual Bandits

Open paper AskPDF arXiv

We propose an algorithmic framework, Offline Estimation to Decisions (OE2D), that efficiently reduces contextual bandit learning with general reward function approximation to offline regression. The framework allows near-optimal regret for contextual bandits with large action spaces with

O(\log T)

calls to an offline regression oracle over

T

rounds, and makes

O(\log\log T)

calls when

T

is known. The design of OE2D algorithm generalizes Falcon~\citep{simchi2022bypassing} and its linear reward version~\citep[][Section 4]{xu2020upper} in that it finds an action distribution that we term ``exploitative F-design'' that simultaneously guarantees low regret and good coverage, striking a balance between exploration and exploitation. Central to our regret analysis is a new complexity measure, the Decision-Offline Estimation Coefficient (DOEC), which we show is small in many settings, including bounded Eluder dimension per-context and the smoothed regret setting. We also establish a relationship between DOEC and Decision Estimation Coefficient (DEC)~\citep{foster2021statistical}, bridging the design principles of offline- and online-oracle efficient contextual bandit algorithms for the first time.

Momentum

Related topics

Latest papers 32

Taming the Monster Every Context: Complexity Measure and Unified Framework for Offline-Oracle Efficient Contextual Bandits

Self-Concordant Perturbations for Linear Bandits