Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization

17 April 2023

Agustinus Kristiadi

Alexander Immer

Runa Eschenhagen

Vincent Fortuin

Papers citing "Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization"

11 / 11 papers shown

Title
One Set to Rule Them All: How to Obtain General Chemical Conditions via Bayesian Optimization over Curried Functions Stefan P. Schmid Ella M. Rajaonson C. Ser Mohammad Haddadnia Shi Xuan Leong Alán Aspuru-Guzik Agustinus Kristiadi Kjell Jorner Felix Strieth-Kalthoff 82 0 0 26 Feb 2025
Uncertainty-Guided Optimization on Large Language Model Search Trees Julia Grosse Ruotian Wu Ahmad Rashid Philipp Hennig Pascal Poupart Agustinus Kristiadi 37 1 0 04 Jul 2024
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? Agustinus Kristiadi Felix Strieth-Kalthoff Sriram Ganapathi Subramanian Vincent Fortuin Pascal Poupart Geoff Pleiss 44 2 0 10 Jun 2024
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? Agustinus Kristiadi Felix Strieth-Kalthoff Marta Skreta Pascal Poupart Alán Aspuru-Guzik Geoff Pleiss 30 21 0 07 Feb 2024
On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective Jonathan Wenger Felix Dangel Agustinus Kristiadi 33 0 0 29 Sep 2023
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization Y. Li Tim G. J. Rudner A. Wilson BDL 26 28 0 31 May 2023
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks Agustinus Kristiadi Runa Eschenhagen Philipp Hennig BDL 21 12 0 20 May 2022
Probing as Quantifying Inductive Bias Alexander Immer Lucas Torroba Hennigen Vincent Fortuin Ryan Cotterell 48 14 0 15 Oct 2021
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam Mohammad Emtiyaz Khan Didrik Nielsen Voot Tangkaratt Wu Lin Y. Gal Akash Srivastava ODL 74 266 0 13 Jun 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015