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New theory on online prediction with infinite memory

By PulseAugur Editorial · [1 sources] ·

This paper introduces a new theoretical framework for online prediction in scenarios with infinite memory, specifically for binary mark prediction driven by exogenous sources. The research establishes sharp minimax regret bounds for summable envelopes, providing precise scales for exponential and polynomial envelopes. A key finding is that retaining only recent inputs can lead to polynomially different regret, and an online Newton predictor is proposed to achieve the upper bound. AI

IMPACT Provides theoretical underpinnings for online prediction algorithms, potentially impacting future AI model development.

RANK_REASON This is a theoretical computer science paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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

New theory on online prediction with infinite memory

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This is a theoretical computer science paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Vaneet Aggarwal ·

    Sharp Minimax Regret for Infinite-Memory Logistic Prediction

    arXiv:2608.26515v1 Announce Type: cross Abstract: We study online prediction for a specific finite-alphabet, exogenously driven source with infinite input memory. Independent Rademacher inputs $(U_t)$ are observed sequentially, and the next binary mark has logit $\sum_{j=1}^{t}\t…

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