Zobrazeno 1 - 10
of 888
pro vyhledávání: '"Terenin, A."'
Autor:
Mostowsky, Peter, Dutordoir, Vincent, Azangulov, Iskander, Jaquier, Noémie, Hutchinson, Michael John, Ravuri, Aditya, Rozo, Leonel, Terenin, Alexander, Borovitskiy, Viacheslav
Kernels are a fundamental technical primitive in machine learning. In recent years, kernel-based methods such as Gaussian processes are becoming increasingly important in applications where quantifying uncertainty is of key interest. In settings that
Externí odkaz:
http://arxiv.org/abs/2407.08086
Publikováno v:
Advances in Neural Information Processing Systems, 2024
Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where gathering data requires use of finite resources, it is desirable to explicitly incorporate function evaluation
Externí odkaz:
http://arxiv.org/abs/2406.20062
Autor:
Lin, Jihao Andreas, Padhy, Shreyas, Antorán, Javier, Tripp, Austin, Terenin, Alexander, Szepesvári, Csaba, Hernández-Lobato, José Miguel, Janz, David
As is well known, both sampling from the posterior and computing the mean of the posterior in Gaussian process regression reduces to solving a large linear system of equations. We study the use of stochastic gradient descent for solving this linear s
Externí odkaz:
http://arxiv.org/abs/2310.20581
Autor:
Brown, Theodore, Marsden, Stephen, Gopakumar, Vignesh, Terenin, Alexander, Ge, Hong, Casson, Francis
Publikováno v:
IEEE Transactions on Plasma Science (2024)
The safety factor profile is a key property in determining the stability of tokamak plasmas. To design the safety factor profile in the United Kingdom's proposed Spherical Tokamak for Energy Production (STEP), we apply multi-objective Bayesian optimi
Externí odkaz:
http://arxiv.org/abs/2310.02669
Autor:
Östling, Andreas, Sargeant, Holli, Xie, Huiyuan, Bull, Ludwig, Terenin, Alexander, Jonsson, Leif, Magnusson, Måns, Steffek, Felix
Publikováno v:
Advances in Neural Information Processing Systems, Datasets and Benchmarks Track, 2023
We introduce the Cambridge Law Corpus (CLC), a dataset for legal AI research. It consists of over 250 000 court cases from the UK. Most cases are from the 21st century, but the corpus includes cases as old as the 16th century. This paper presents the
Externí odkaz:
http://arxiv.org/abs/2309.12269
Publikováno v:
Advances in Neural Information Processing Systems, 2023
Gaussian processes are used in many machine learning applications that rely on uncertainty quantification. Recently, computational tools for working with these models in geometric settings, such as when inputs lie on a Riemannian manifold, have been
Externí odkaz:
http://arxiv.org/abs/2309.10918
Autor:
Cosier, Lucas, Iordan, Rares, Zwane, Sicelukwanda, Franzese, Giovanni, Wilson, James T., Deisenroth, Marc Peter, Terenin, Alexander, Bekiroglu, Yasemin
Publikováno v:
Artificial Intelligence and Statistics, 2024
To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and prevent
Externí odkaz:
http://arxiv.org/abs/2309.00854
Autor:
Lin, Jihao Andreas, Antorán, Javier, Padhy, Shreyas, Janz, David, Hernández-Lobato, José Miguel, Terenin, Alexander
Publikováno v:
Advances in Neural Information Processing Systems, 2023
Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems. In general, this has a cubic cost in dataset size and is sensitive to conditionin
Externí odkaz:
http://arxiv.org/abs/2306.11589
Publikováno v:
Journal of Machine Learning Research, 2024
Gaussian processes are arguably the most important class of spatiotemporal models within machine learning. They encode prior information about the modeled function and can be used for exact or approximate Bayesian learning. In many applications, part
Externí odkaz:
http://arxiv.org/abs/2301.13088
Autor:
Terenin, Alexander, Burt, David R., Artemev, Artem, Flaxman, Seth, van der Wilk, Mark, Rasmussen, Carl Edward, Ge, Hong
Publikováno v:
Journal of Machine Learning Research, 2024
Gaussian processes are frequently deployed as part of larger machine learning and decision-making systems, for instance in geospatial modeling, Bayesian optimization, or in latent Gaussian models. Within a system, the Gaussian process model needs to
Externí odkaz:
http://arxiv.org/abs/2210.07893