mlsanityData to 13 Oct 2026

When Determinants Are Not Enough: Private Rare Switching

arXiv 2605.23131 ↗First posted 22 May 2026cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

In this note, I would like to share a small research moment where Codex helped me find the right way to adapt rare switching to the private setting. The standard determinant-based update rule in linear bandits and RL works beautifully because the design matrix grows monotonically. But once Gaussian noise is added for privacy, this monotonicity can fail, and the usual analysis no longer goes through. The key reason is that determinant growth controls volume, while regret analysis needs control of the worst direction. To address this, Codex comes up with a different rare-switching rule based on the generalized Rayleigh quotient, which restores logarithmic policy updates and the desired confidence-width comparison up to a constant factor. I present my manually clean-up version of the proof here as well as some personal reflection on this example.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 3 of its 4 references that are in the map. First posted on arXiv 22 May 2026.

Builds on

3 of 4 references.

References · 3

  1. 2022Shuffle Private Linear Contextual Banditscited by 27
  2. 2023On Differentially Private Federated Linear Contextual Banditscited by 14
  3. 2026Towards Differentially Private Reinforcement Learning with General Function Approximationcited by 2

Built on by

Not cited yet.