Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Padhy, Shreyas"'
Autor:
Denker, Alexander, Vargas, Francisco, Padhy, Shreyas, Didi, Kieran, Mathis, Simon, Dutordoir, Vincent, Barbano, Riccardo, Mathieu, Emile, Komorowska, Urszula Julia, Lio, Pietro
Generative modelling paradigms based on denoising diffusion processes have emerged as a leading candidate for conditional sampling in inverse problems. In many real-world applications, we often have access to large, expensively trained unconditional
Externí odkaz:
http://arxiv.org/abs/2406.01781
Autor:
Lin, Jihao Andreas, Padhy, Shreyas, Mlodozeniec, Bruno, Antorán, Javier, Hernández-Lobato, José Miguel
Scaling hyperparameter optimisation to very large datasets remains an open problem in the Gaussian process community. This paper focuses on iterative methods, which use linear system solvers, like conjugate gradients, alternating projections or stoch
Externí odkaz:
http://arxiv.org/abs/2405.18457
Gaussian processes are a versatile probabilistic machine learning model whose effectiveness often depends on good hyperparameters, which are typically learned by maximising the marginal likelihood. In this work, we consider iterative methods, which u
Externí odkaz:
http://arxiv.org/abs/2405.18328
Autor:
Allingham, James Urquhart, Mlodozeniec, Bruno Kacper, Padhy, Shreyas, Antorán, Javier, Krueger, David, Turner, Richard E., Nalisnick, Eric, Hernández-Lobato, José Miguel
Correctly capturing the symmetry transformations of data can lead to efficient models with strong generalization capabilities, though methods incorporating symmetries often require prior knowledge. While recent advancements have been made in learning
Externí odkaz:
http://arxiv.org/abs/2403.01946
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
Connecting optimal transport and variational inference, we present a principled and systematic framework for sampling and generative modelling centred around divergences on path space. Our work culminates in the development of the \emph{Controlled Mo
Externí odkaz:
http://arxiv.org/abs/2307.01050
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
Neural kernels have drastically increased performance on diverse and nonstandard data modalities but require significantly more compute, which previously limited their application to smaller datasets. In this work, we address this by massively parall
Externí odkaz:
http://arxiv.org/abs/2303.05420
Autor:
Antorán, Javier, Padhy, Shreyas, Barbano, Riccardo, Nalisnick, Eric, Janz, David, Hernández-Lobato, José Miguel
Large-scale linear models are ubiquitous throughout machine learning, with contemporary application as surrogate models for neural network uncertainty quantification; that is, the linearised Laplace method. Alas, the computational cost associated wit
Externí odkaz:
http://arxiv.org/abs/2210.04994
Autor:
Liu, Jeremiah Zhe, Padhy, Shreyas, Ren, Jie, Lin, Zi, Wen, Yeming, Jerfel, Ghassen, Nado, Zack, Snoek, Jasper, Tran, Dustin, Lakshminarayanan, Balaji
Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular approaches to estimate predictive
Externí odkaz:
http://arxiv.org/abs/2205.00403