Robust Multi-Objective Bayesian Optimization Under Input Noise 论文链接: https://arxiv.org/abs/2202.07549 项目链接: https://github.com/facebookresearch/robust_mobo 本文是 facebook 发表于 ICML 2022 的一篇工作,其在理论角度上对有输入噪声的多目标贝叶斯优化进行了分析。 引言 本文面向多目标优化的输入噪...
This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the bal...
We propose a novel transfer learning method to obtain customized optimizers within the well-established framework of Bayesian optimization, allowing our algorithm to utilize the proven generalization capabilities of Gaussian processes. Using reinforcement learning to meta-train an acquisition function (AF) ...
relationship between tasks during the optimization process using Bayesian Neural Networks. As such, their method is somewhat of a hybrid of the previous two approaches. Golovin et al. [58] assume a sequence order (e.g., time) across tasks. It builds a stack of GP regressors, one per task...
Bayesian optimization-based meta-learning Use ensembles to model non-gaussian posterior Sample Parameter Vectors Sample parameter vectors with a procedure like Hamiltonian Monte Carlo to model non-Gaussian posterior over all parameters: Methods Summary ...
scikit-learnhyperparameter-optimizationbayesian-optimizationhyperparameter-tuningautomlautomated-machine-learningsmacmeta-learninghyperparameter-searchmetalearning UpdatedJan 22, 2025 Python learnables/learn2learn Star2.8k Code Issues Pull requests A PyTorch Library for Meta-learning Research ...
Historically, finding promising hyperparameters has mainly relied on various search algorithms, such as random search (e.g., [4]), hyperband (e.g., [5]), and Bayesian optimization (e.g., [6], [7]). These classical search algorithms and their variants [8], [9], [10] focus primaril...
1.2 B.2 Optimization-based 1.2.1 B.2.1 MAML MAML (Finn et al., 2017) is a meta-learning approach that is agnostic to specific models, making it compatible with any model trained using gradient descent. It can be applied to a variety of learning problems, with the explicit goal of train...
(onlylearningto compare) Hybrid 最后讲一下融合模型。LEO(LatentEmbeddingOptimization) 如上,我们的在 inner训练时(训练元学习的能力时),讲数据集投影到一个较小维度的空间中,这个空间对应着网络参数的空间。 Bayesianmeta-learning如上,我们的目标本来是想区分“笑”与 ...
Meta-Learning Priors for Safe Bayesian Optimization In robotics, optimizing controller parameters under safety constraints is an important challenge. Safe Bayesian optimization (BO) quantifies uncertainty in the objective and constraints to safely guide exploration in such settings. Hand-designing a ...