منابع مشابه
Adaptive Online Learning
We propose a general framework for studying adaptive regret bounds in the online learning framework, including model selection bounds and data-dependent bounds. Given a dataor model-dependent bound we ask, “Does there exist some algorithm achieving this bound?” We show that modifications to recently introduced sequential complexity measures can be used to answer this question by providing suffi...
متن کاملStrongly Adaptive Online Learning
Strongly adaptive algorithms are algorithms whose performance on every time interval is close to optimal. We present a reduction that can transform standard low-regret algorithms to strongly adaptive. As a consequence, we derive simple, yet efficient, strongly adaptive algorithms for a handful of problems.
متن کاملSupplement: Strongly Adaptive Online Learning
Proof (of Lemma 1) The proof is by induction on t. For t = 1, we have˜W 1 = ˜ w 1 ([1, 1]) = 1. Next, we assume that the claim holds for any t ≤ t and prove it for t+1. Since |{[q, s] ∈ I : q = t}| ≤ log(t)+ 1 for all t ≥ 1, we have˜W t+1 = I=[q,s] ∈I˜w t+1 (I) = I=[t+1,s] ∈I˜w t+1 (I) + I=[q,s]∈I: q≤t˜w t+1 (I) ≤ log(t + 1) + 1 + I=[q,s]∈I: q≤t˜w t+1 (I) .
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Almeida et al. have recently proposed online algorithms for local step size adaptation in nonlinear systems trained by gradient descent. Here we develop an alternative to their approach by extending Sutton’s work on linear systems to the general, nonlinear case. The resulting algorithms are computationally little more expensive than other acceleration techniques, do not assume statistical indep...
متن کاملAdaptive Communication Bounds for Distributed Online Learning
We consider distributed online learning protocols that control the exchange of information between local learners in a round-based learning scenario. The learning performance of such a protocol is intuitively optimal if approximately the same loss is incurred as in a hypothetical serial setting. If a protocol accomplishes this, it is inherently impossible to achieve a strong communication bound...
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ژورنال
عنوان ژورنال: International Journal of Web-Based Learning and Teaching Technologies
سال: 2020
ISSN: 1548-1093,1548-1107
DOI: 10.4018/ijwltt.2020100102