Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Wenger, Jonathan P."'
Autor:
Wenger, Jonathan, Wu, Kaiwen, Hennig, Philipp, Gardner, Jacob R., Pleiss, Geoff, Cunningham, John P.
Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory. While many approximations exist, all incur inevitable approximation error. Recent work accounts for this error in the form of c
Externí odkaz:
http://arxiv.org/abs/2411.01036
Kalman filtering and smoothing are the foundational mechanisms for efficient inference in Gauss-Markov models. However, their time and memory complexities scale prohibitively with the size of the state space. This is particularly problematic in spati
Externí odkaz:
http://arxiv.org/abs/2405.08971
Bayesian Generalized Linear Models (GLMs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, and are widely used in practice. However, exact inference in GLMs is prohibitively expensive for large datasets, th
Externí odkaz:
http://arxiv.org/abs/2310.20285
Training and inference in Gaussian processes (GPs) require solving linear systems with $n\times n$ kernel matrices. To address the prohibitive $\mathcal{O}(n^3)$ time complexity, recent work has employed fast iterative methods, like conjugate gradien
Externí odkaz:
http://arxiv.org/abs/2310.17137
The neural tangent kernel (NTK) has garnered significant attention as a theoretical framework for describing the behavior of large-scale neural networks. Kernel methods are theoretically well-understood and as a result enjoy algorithmic benefits, whi
Externí odkaz:
http://arxiv.org/abs/2310.00137
Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical processes such as heat transfer, electromagnetism, and wave propagation. In practice, specialized numerical methods based o
Externí odkaz:
http://arxiv.org/abs/2212.12474
Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation methods have been developed, which inevitably introduce approximation error. This additional source of uncertainty, due to limited computation, is en
Externí odkaz:
http://arxiv.org/abs/2205.15449
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
Gaussian process hyperparameter optimization requires linear solves with, and log-determinants of, large kernel matrices. Iterative numerical techniques are becoming popular to scale to larger datasets, relying on the conjugate gradient method (CG) f
Externí odkaz:
http://arxiv.org/abs/2107.00243
Autor:
Wenger, Jonathan, Hennig, Philipp
Linear systems are the bedrock of virtually all numerical computation. Machine learning poses specific challenges for the solution of such systems due to their scale, characteristic structure, stochasticity and the central role of uncertainty in the
Externí odkaz:
http://arxiv.org/abs/2010.09691