{"ID":2845870,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03606","arxiv_id":"2511.03606","title":"Vector-valued self-normalized concentration inequalities beyond sub-Gaussianity","abstract":"The study of self-normalized processes plays a crucial role in a wide range of applications, from sequential decision-making to econometrics. While the behavior of self-normalized concentration has been widely investigated for scalar-valued processes, vector-valued processes remain comparatively underexplored, especially outside of the sub-Gaussian framework. In this contribution, we provide concentration bounds for self-normalized processes with light tails beyond sub-Gaussianity (such as Bennett or Bernstein bounds). We illustrate the relevance of our results in the context of online linear regression, with applications in (kernelized) linear bandits.","short_abstract":"The study of self-normalized processes plays a crucial role in a wide range of applications, from sequential decision-making to econometrics. While the behavior of self-normalized concentration has been widely investigated for scalar-valued processes, vector-valued processes remain comparatively underexplored, especial...","url_abs":"https://arxiv.org/abs/2511.03606","url_pdf":"https://arxiv.org/pdf/2511.03606v2","authors":"[\"Diego Martinez-Taboada\",\"Tomas Gonzalez\",\"Aaditya Ramdas\"]","published":"2025-11-05T16:27:02Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\",\"math.ST\"]","methods":"[]","has_code":false}