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of 21
pro vyhledávání: '"Mahsereci, Maren"'
Gaussian process (GP) regression is a Bayesian nonparametric method for regression and interpolation, offering a principled way of quantifying the uncertainties of predicted function values. For the quantified uncertainties to be well-calibrated, how
Externí odkaz:
http://arxiv.org/abs/2307.07466
Autor:
Wenger, Jonathan, Krämer, Nicholas, Pförtner, Marvin, Schmidt, Jonathan, Bosch, Nathanael, Effenberger, Nina, Zenn, Johannes, Gessner, Alexandra, Karvonen, Toni, Briol, François-Xavier, Mahsereci, Maren, Hennig, Philipp
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear algebra, optimization, integration and differential equation simulation. PNMs naturally incorporate prior information abo
Externí odkaz:
http://arxiv.org/abs/2112.02100
Bayesian quadrature (BQ) is a model-based numerical integration method that is able to increase sample efficiency by encoding and leveraging known structure of the integration task at hand. In this paper, we explore priors that encode invariance of t
Externí odkaz:
http://arxiv.org/abs/2112.01578
Autor:
Paleyes, Andrei, Pullin, Mark, Mahsereci, Maren, McCollum, Cliff, Lawrence, Neil D., Gonzalez, Javier
Decision making in uncertain scenarios is an ubiquitous challenge in real world systems. Tools to deal with this challenge include simulations to gather information and statistical emulation to quantify uncertainty. The machine learning community has
Externí odkaz:
http://arxiv.org/abs/2110.13293
Autor:
Kersting, Hans, Mahsereci, Maren
Gaussian ODE filtering is a probabilistic numerical method to solve ordinary differential equations (ODEs). It computes a Bayesian posterior over the solution from evaluations of the vector field defining the ODE. Its most popular version, which empl
Externí odkaz:
http://arxiv.org/abs/2007.09118
Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical method to solve integrals of expensive-to-evaluate black-box functions, yet so far,active BQ learning schemes focus merely on the integrand itself as information source, and do no
Externí odkaz:
http://arxiv.org/abs/1903.11331
Autor:
Mahsereci, Maren, Hennig, Philipp
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict
Externí odkaz:
http://arxiv.org/abs/1703.10034
Early stopping is a widely used technique to prevent poor generalization performance when training an over-expressive model by means of gradient-based optimization. To find a good point to halt the optimizer, a common practice is to split the dataset
Externí odkaz:
http://arxiv.org/abs/1703.09580
Autor:
Mahsereci, Maren, Hennig, Philipp
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict
Externí odkaz:
http://arxiv.org/abs/1502.02846
Gaussian process (GP) regression is a Bayesian nonparametric method for regression and interpolation, offering a principled way of quantifying the uncertainties of predicted function values. For the quantified uncertainties to be well-calibrated, how
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a977134a2243471b1347e9862900237