New paper explores information flow in martingales, unifying concentration inequalities

A new paper published on arXiv details advancements in understanding information flow within the path space of nonnegative martingales. The research introduces exact variational identities that apply even at arbitrary random times, unifying and extending classical concentration inequalities like Ville and PAC-Bayesian learning. The work also quantifies the 'peeking penalty' associated with anticipation in arbitrary random times and explores how geometric mixtures of test martingales can benefit multi-model safe testing. AI

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

• Akshay Balsubramani
• alphaXiv
• arXiv
• Azuma-Hoeffding
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• Influence Flower
• linear programming
• PAC-bayesian learning
• ScienceCast
• Ville

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →