Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Mutný, Mojmír"'
Bayesian optimization (BO) is a powerful framework to optimize black-box expensive-to-evaluate functions via sequential interactions. In several important problems (e.g. drug discovery, circuit design, neural architecture search, etc.), though, such
Externí odkaz:
http://arxiv.org/abs/2409.18582
Autor:
Folch, Jose Pablo, Tsay, Calvin, Lee, Robert M, Shafei, Behrang, Ormaniec, Weronika, Krause, Andreas, van der Wilk, Mark, Misener, Ruth, Mutný, Mojmír
Bayesian optimization is a methodology to optimize black-box functions. Traditionally, it focuses on the setting where you can arbitrarily query the search space. However, many real-life problems do not offer this flexibility; in particular, the sear
Externí odkaz:
http://arxiv.org/abs/2402.08406
Certifiable, adaptive uncertainty estimates for unknown quantities are an essential ingredient of sequential decision-making algorithms. Standard approaches rely on problem-dependent concentration results and are limited to a specific combination of
Externí odkaz:
http://arxiv.org/abs/2311.04402
In reinforcement learning (RL), rewards of states are typically considered additive, and following the Markov assumption, they are $\textit{independent}$ of states visited previously. In many important applications, such as coverage control, experime
Externí odkaz:
http://arxiv.org/abs/2307.13372
A key challenge in science and engineering is to design experiments to learn about some unknown quantity of interest. Classical experimental design optimally allocates the experimental budget to maximize a notion of utility (e.g., reduction in uncert
Externí odkaz:
http://arxiv.org/abs/2206.14332
Autor:
Mutný, Mojmír, Krause, Andreas
Publikováno v:
NeurIPS 2022
Optimal experimental design seeks to determine the most informative allocation of experiments to infer an unknown statistical quantity. In this work, we investigate the optimal design of experiments for {\em estimation of linear functionals in reprod
Externí odkaz:
http://arxiv.org/abs/2205.13627
Autor:
Kirschner, Johannes, Mutný, Mojmir, Krause, Andreas, de Portugal, Jaime Coello, Hiller, Nicole, Snuverink, Jochem
Tuning machine parameters of particle accelerators is a repetitive and time-consuming task that is challenging to automate. While many off-the-shelf optimization algorithms are available, in practice their use is limited because most methods do not a
Externí odkaz:
http://arxiv.org/abs/2203.13968
In Bayesian Optimization (BO) we study black-box function optimization with noisy point evaluations and Bayesian priors. Convergence of BO can be greatly sped up by batching, where multiple evaluations of the black-box function are performed in a sin
Externí odkaz:
http://arxiv.org/abs/2110.11665
Autor:
Mutný, Mojmír, Krause, Andreas
We study adaptive sensing of Cox point processes, a widely used model from spatial statistics. We introduce three tasks: maximization of captured events, search for the maximum of the intensity function and learning level sets of the intensity functi
Externí odkaz:
http://arxiv.org/abs/2110.11181
The increasing availability of massive data sets poses a series of challenges for machine learning. Prominent among these is the need to learn models under hardware or human resource constraints. In such resource-constrained settings, a simple yet po
Externí odkaz:
http://arxiv.org/abs/2109.12534