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pro vyhledávání: '"Lee, Eric Hans"'
Publikováno v:
Knowledge-Based Systems Volume 273, 3 August 2023, 110604
Gradient boosted trees (GBTs) are ubiquitous models used by researchers, machine learning (ML) practitioners, and data scientists because of their robust performance, interpretable behavior, and ease-of-use. One critical challenge in training GBTs is
Externí odkaz:
http://arxiv.org/abs/2307.04849
Publikováno v:
2022 Winter Simulation Conference
Efficiently solving multi-objective optimization problems for simulation optimization of important scientific and engineering applications such as materials design is becoming an increasingly important research topic. This is due largely to the expen
Externí odkaz:
http://arxiv.org/abs/2306.13780
The Bayesian transformed Gaussian process (BTG) model, proposed by Kedem and Oliviera, is a fully Bayesian counterpart to the warped Gaussian process (WGP) and marginalizes out a joint prior over input warping and kernel hyperparameters. This fully B
Externí odkaz:
http://arxiv.org/abs/2210.10973
Bayesian optimization (BO) is a popular method for optimizing expensive-to-evaluate black-box functions. BO budgets are typically given in iterations, which implicitly assumes each evaluation has the same cost. In fact, in many BO applications, evalu
Externí odkaz:
http://arxiv.org/abs/2106.06079
Bayesian optimization (BO) is a class of global optimization algorithms, suitable for minimizing an expensive objective function in as few function evaluations as possible. While BO budgets are typically given in iterations, this implicitly measures
Externí odkaz:
http://arxiv.org/abs/2003.10870
Bayesian optimization (BO) is a class of sample-efficient global optimization methods, where a probabilistic model conditioned on previous observations is used to determine future evaluations via the optimization of an acquisition function. Most acqu
Externí odkaz:
http://arxiv.org/abs/2002.10539
Publikováno v:
Advances in Neural Information Processing Systems 32 (NIPS), 2018
Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction. Fitting a GP to function values and derivatives at $n$ points in $d$ dimensions r
Externí odkaz:
http://arxiv.org/abs/1810.12283
Autor:
Kalantari, Bahman, Lee, Eric Hans
Publikováno v:
Journal of Mathematics & the Arts; Dec2019, Vol. 13 Issue 4, p336-352, 17p