Conservative Online Convex Optimization

نویسندگان

چکیده

Online learning algorithms often have the issue of exhibiting poor performance during initial stages optimization procedure, which in practical applications might dissuade potential users from deploying such solutions. In this paper, we study a novel setting, namely conservative online convex optimization, are optimizing sequence loss functions under constraint that to perform at least as well known default strategy throughout entire process, a.k.a. conservativeness constraint. To address problem design meta-algorithm, Conservative Projection (CP), converts any no-regret algorithm for into one that, same time, satisfies and maintains regret order. Finally, run an extensive experimental campaign, comparing analyzing our meta-algorithm with state-of-the-art algorithms.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2021

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-030-86486-6_2