# On the Minimax Regret for Linear Bandits in a wide variety of Action Spaces

arXiv: https://arxiv.org/abs/2301.03597 · HTML: https://mlsanity.com/paper/2301.03597/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 1 of 1)

- [Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference](https://mlsanity.com/paper/2207.11597/) (2022)

## Built on by (most cited first, 1 of 1)

- [Tangential Randomization in Linear Bandits (TRAiL): Guaranteed Inference and Regret Bounds](https://mlsanity.com/paper/2411.12154/) (2024)

## Abstract (arXiv)

As noted in the works of citelattimore2020bandit, it has been mentioned that it is an open problem to characterize the minimax regret of linear bandits in a wide variety of action spaces. In this article we present an optimal regret lower bound for a wide class of convex action spaces.