# Meta Learning in Bandits within Shared Affine Subspaces

arXiv: https://arxiv.org/abs/2404.00688 · HTML: https://mlsanity.com/paper/2404.00688/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 48 references that are in the map.
- First posted on arXiv 31 Mar 2024.

## Builds on (most cited first, 22 of 22)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Online Meta-Learning](https://mlsanity.com/paper/1902.08438/) (2019)
- [PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees](https://mlsanity.com/paper/2002.05551/) (2020)
- [Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory](https://mlsanity.com/paper/1711.01244/) (2017)
- [Meta-Thompson Sampling](https://mlsanity.com/paper/2102.06129/) (2021)
- [Impact of Representation Learning in Linear Bandits](https://mlsanity.com/paper/2010.06531/) (2020)
- [Meta-learning with Stochastic Linear Bandits](https://mlsanity.com/paper/2005.08531/) (2020)
- [Meta Dynamic Pricing: Transfer Learning Across Experiments](https://mlsanity.com/paper/1902.10918/) (2019)
- [Sequential Transfer in Multi-armed Bandit with Finite Set of Models](https://mlsanity.com/paper/1307.6887/) (2013)
- [No Regrets for Learning the Prior in Bandits](https://mlsanity.com/paper/2107.06196/) (2021)
- [Multi-task Representation Learning with Stochastic Linear Bandits](https://mlsanity.com/paper/2202.10066/) (2022)
- [Laplacian-regularized graph bandits: Algorithms and theoretical analysis](https://mlsanity.com/paper/1907.05632/) (2019)
- [Metalearning Linear Bandits by Prior Update](https://mlsanity.com/paper/2107.05320/) (2021)
- [Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?](https://mlsanity.com/paper/1511.03688/) (2015)
- [Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms](https://mlsanity.com/paper/2202.13001/) (2022)
- [Meta Representation Learning with Contextual Linear Bandits](https://mlsanity.com/paper/2205.15100/) (2022)
- [Differentiable Linear Bandit Algorithm](https://mlsanity.com/paper/2006.03000/) (2020)
- [Meta-Learning Hypothesis Spaces for Sequential Decision-making](https://mlsanity.com/paper/2202.00602/) (2022)
- [Meta-Learning Adversarial Bandits](https://mlsanity.com/paper/2205.14128/) (2022)
- [Lifelong Bandit Optimization: No Prior and No Regret](https://mlsanity.com/paper/2210.15513/) (2022)
- [Linear Combinatorial Semi-Bandit with Causally Related Rewards](https://mlsanity.com/paper/2212.12923/) (2022)
- [Hypothesis Transfer in Bandits by Weighted Models](https://mlsanity.com/paper/2211.07387/) (2022)

## Built on by (most cited first, 4 of 4)

- [Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis](https://mlsanity.com/paper/2404.12481/) (2024)
- [Beyond Task Diversity: Provable Representation Transfer for Sequential Multi-Task Linear Bandits](https://mlsanity.com/paper/2501.13390/) (2025)
- [Invariance-Based Dynamic Regret Minimization](https://mlsanity.com/paper/2603.03843/) (2026)
- [Meta-LinEXP3: Online-within-Online Learning for Adversarial Linear Contextual Bandits](https://mlsanity.com/paper/2609.09907/) (2026)

## Abstract (arXiv)

We study the problem of meta-learning several contextual stochastic bandits tasks by leveraging their concentration around a low-dimensional affine subspace, which we learn via online principal component analysis to reduce the expected regret over the encountered bandits. We propose and theoretically analyze two strategies that solve the problem: One based on the principle of optimism in the face of uncertainty and the other via Thompson sampling. Our framework is generic and includes previously proposed approaches as special cases. Besides, the empirical results show that our methods significantly reduce the regret on several bandit tasks.