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pro vyhledávání: '"Ao, Ruicheng"'
In this work, we introduce a new framework for active experimentation, the Prediction-Guided Active Experiment (PGAE), which leverages predictions from an existing machine learning model to guide sampling and experimentation. Specifically, at each ti
Externí odkaz:
http://arxiv.org/abs/2411.12036
We study a two-stage online reusable resource allocation problem over T days involving advance reservations and walk-ins. Each day begins with a reservation stage (Stage I), where reservation requests arrive sequentially. When service starts (Stage I
Externí odkaz:
http://arxiv.org/abs/2410.15245
We consider the problem of sequentially conducting multiple experiments where each experiment corresponds to a hypothesis testing task. At each time point, the experimenter must make an irrevocable decision of whether to reject the null hypothesis (o
Externí odkaz:
http://arxiv.org/abs/2402.11425
Finding equilibria via gradient play in competitive multi-agent games has been attracting a growing amount of attention in recent years, with emphasis on designing efficient strategies where the agents operate in a decentralized and symmetric manner
Externí odkaz:
http://arxiv.org/abs/2211.08980
This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative log-probability losses. Such problems arise in various machine learning and signal processing applications. By introduci
Externí odkaz:
http://arxiv.org/abs/2207.07287
Akademický článek
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Autor:
Ahmad, Badrul Hisham bin, Subramaniyam, Kannimuthu, Li, Tianfu, Chen, Tangjun, Li, Kunyang, Ao, Ruicheng
Publikováno v:
Proceedings of SPIE; November 2023, Vol. 12937 Issue: 1 p129371L-129371L-6, 1164346p
Publikováno v:
Proceedings of SPIE; 6/23/2024, Vol. 12937, p129371L-129371L-6, 1p