Catching a Moving Subspace: Low-Rank Bandits Beyond Stationarity
The paper titled 'Catching a Moving Subspace: Low-Rank Bandits Beyond Stationarity' explores the challenges of low-rank bandits in dynamic environments. It presents a new algorithm that adapts to changes in the underlying subspace while maintaining efficiency. The authors demonstrate the effectiveness of their approach through empirical results across various benchmarks.
- ▪The study focuses on piecewise-stationary low-rank linear contextual bandits with scalar feedback.
- ▪The proposed algorithm, SPSC, interleaves isotropic probes with exploitation strategies to manage dynamic regret.
- ▪Empirical results show that SPSC outperforms existing non-stationary and low-rank baselines in specific conditions.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20269 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | jqE-rAm9ehgd |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Machine Learning arXiv:2605.20269 (cs) [Submitted on 18 May 2026] Title:Catching a Moving Subspace: Low-Rank Bandits Beyond Stationarity Authors:Hamed Khosravi, Xiaoming Huo View a PDF of the paper titled Catching a Moving Subspace: Low-Rank Bandits Beyond Stationarity, by Hamed Khosravi and 1 other authors View PDF HTML (experimental) Abstract:Many bandit deployments (recommendation, clinical dosing, ad targeting) share two facts prior work handles only in isolation: rewards live on a low-dimensional latent subspace, and that subspace drifts. Stationary low-rank bandits exploit rank but break under subspace change; non-stationary linear bandits adapt to drift but pay ambient rate $\widetilde{O}(d\sqrt{T})$.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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