The Promises and Pitfalls of Deep Kernel Learning

The Promises and Pitfalls of Deep Kernel Learning

24 February 2021

Sebastian W. Ober

Mark van der Wilk

Papers citing "The Promises and Pitfalls of Deep Kernel Learning"

17 / 17 papers shown

Title
GOLLuM: Gaussian Process Optimized LLMs -- Reframing LLM Finetuning through Bayesian Optimization Bojana Ranković P. Schwaller BDL 128 0 0 08 Apr 2025
Image Classification with Deep Reinforcement Active Learning Mingyuan Jiu Xuguang Song H. Sahbi Shupan Li Yan Chen Wei Guo Lihua Guo Mingliang Xu VLM 24 0 0 31 Dec 2024
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach Mahrokh Ghoddousi Boroujeni Andreas Krause Giancarlo Ferrari-Trecate FedML 32 3 0 16 Jan 2024
Multiple output samples per input in a single-output Gaussian process J. H. M. Wong Huayun Zhang Nancy F. Chen GP 11 2 0 05 Jun 2023
Counterfactual Learning with Multioutput Deep Kernels A. Caron G. Baio I. Manolopoulou BDL CML OffRL 15 1 0 20 Nov 2022
Distributional Gaussian Processes Layers for Out-of-Distribution Detection S. Popescu D. Sharp James H. Cole Konstantinos Kamnitsas Ben Glocker OOD 21 0 0 27 Jun 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi Pavel Izmailov Gregory W. Benton Micah Goldblum A. Wilson UQCV BDL 50 56 0 23 Feb 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Alexander Immer Tycho F. A. van der Ouderaa Gunnar Rätsch Vincent Fortuin Mark van der Wilk BDL 24 44 0 22 Feb 2022
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes Hugh Dance Brooks Paige GP 20 9 0 08 Nov 2021
Meta-learning Amidst Heterogeneity and Ambiguity Kyeongryeol Go Seyoung Yun 22 1 0 05 Jul 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 29 124 0 14 May 2021
No Free Lunch for Approximate MCMC J. Johndrow Natesh S. Pillai Aaron Smith 64 18 0 23 Oct 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober Laurence Aitchison BDL 13 60 0 17 May 2020
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks John Bradshaw A. G. Matthews Zoubin Ghahramani BDL AAML 60 171 0 08 Jul 2017
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 273 2,886 0 15 Sep 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,134 0 06 Jun 2015
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 84 271 0 24 Feb 2014