Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Balandat, Maximilian"'
Autor:
Yang, Shangda, Zankin, Vitaly, Balandat, Maximilian, Scherer, Stefan, Carlberg, Kevin, Walton, Neil, Law, Kody J. H.
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations. Often these expectations must be computed by Monte Carlo (MC). The co
Externí odkaz:
http://arxiv.org/abs/2402.02111
Bayesian Optimization (BO) is a technique for sample-efficient black-box optimization that employs probabilistic models to identify promising input locations for evaluation. When dealing with composite-structured functions, such as f=g o h, evaluatin
Externí odkaz:
http://arxiv.org/abs/2311.02213
Autor:
Buathong, Poompol, Wan, Jiayue, Astudillo, Raul, Daulton, Samuel, Balandat, Maximilian, Frazier, Peter I.
Bayesian optimization is a powerful framework for optimizing functions that are expensive or time-consuming to evaluate. Recent work has considered Bayesian optimization of function networks (BOFN), where the objective function is given by a network
Externí odkaz:
http://arxiv.org/abs/2311.02146
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian optimization and has found countless successful applications, but its performance is often exceeded by that of more recent methods. Notably, EI and its variants,
Externí odkaz:
http://arxiv.org/abs/2310.20708
Autor:
Deshwal, Aryan, Ament, Sebastian, Balandat, Maximilian, Bakshy, Eytan, Doppa, Janardhan Rao, Eriksson, David
We consider the problem of optimizing expensive black-box functions over high-dimensional combinatorial spaces which arises in many science, engineering, and ML applications. We use Bayesian Optimization (BO) and propose a novel surrogate modeling ap
Externí odkaz:
http://arxiv.org/abs/2303.01774
Autor:
Daulton, Samuel, Wan, Xingchen, Eriksson, David, Balandat, Maximilian, Osborne, Michael A., Bakshy, Eytan
Optimizing expensive-to-evaluate black-box functions of discrete (and potentially continuous) design parameters is a ubiquitous problem in scientific and engineering applications. Bayesian optimization (BO) is a popular, sample-efficient method that
Externí odkaz:
http://arxiv.org/abs/2210.10199
Autor:
Daulton, Samuel, Cakmak, Sait, Balandat, Maximilian, Osborne, Michael A., Zhou, Enlu, Bakshy, Eytan
Bayesian optimization (BO) is a sample-efficient approach for tuning design parameters to optimize expensive-to-evaluate, black-box performance metrics. In many manufacturing processes, the design parameters are subject to random input noise, resulti
Externí odkaz:
http://arxiv.org/abs/2202.07549
Bayesian optimization (BO) is a sample-efficient approach to optimizing costly-to-evaluate black-box functions. Most BO methods ignore how evaluation costs may vary over the optimization domain. However, these costs can be highly heterogeneous and ar
Externí odkaz:
http://arxiv.org/abs/2111.06537
Autor:
Wu, Carole-Jean, Raghavendra, Ramya, Gupta, Udit, Acun, Bilge, Ardalani, Newsha, Maeng, Kiwan, Chang, Gloria, Behram, Fiona Aga, Huang, James, Bai, Charles, Gschwind, Michael, Gupta, Anurag, Ott, Myle, Melnikov, Anastasia, Candido, Salvatore, Brooks, David, Chauhan, Geeta, Lee, Benjamin, Lee, Hsien-Hsin S., Akyildiz, Bugra, Balandat, Maximilian, Spisak, Joe, Jain, Ravi, Rabbat, Mike, Hazelwood, Kim
This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the carbon footprint of AI computing by examining the model development
Externí odkaz:
http://arxiv.org/abs/2111.00364
Many real world scientific and industrial applications require optimizing multiple competing black-box objectives. When the objectives are expensive-to-evaluate, multi-objective Bayesian optimization (BO) is a popular approach because of its high sam
Externí odkaz:
http://arxiv.org/abs/2109.10964