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MALIBO: Meta-learning for Likelihood-free Bayesian Optimization

7 July 2023

Papers citing "MALIBO: Meta-learning for Likelihood-free Bayesian Optimization"

24 / 24 papers shown

Title
Optimizing Hyperparameters with Conformal Quantile Regression David Salinas Jacek Golebiowski Aaron Klein Matthias Seeger Cédric Archambeau 78 9 0 05 May 2023
Batch Bayesian optimisation via density-ratio estimation with guarantees Rafael Oliveira Louis C. Tiao Fabio Ramos 84 7 0 22 Sep 2022
Pre-training helps Bayesian optimization too Zehao Wang George E. Dahl Kevin Swersky Chansoo Lee Zelda E. Mariet Zachary Nado Justin Gilmer Jasper Snoek Zoubin Ghahramani 60 9 0 07 Jul 2022
A General Recipe for Likelihood-free Bayesian Optimization Jiaming Song Lantao Yu Willie Neiswanger Stefano Ermon 64 24 0 27 Jun 2022
Towards Learning Universal Hyperparameter Optimizers with Transformers Yutian Chen Xingyou Song Chansoo Lee Zehao Wang Qiuyi Zhang ... Greg Kochanski Arnaud Doucet MarcÁurelio Ranzato Sagi Perel Nando de Freitas 90 65 0 26 May 2022
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO Katharina Eggensperger Philip Muller Neeratyoy Mallik Matthias Feurer René Sass Aaron Klein Noor H. Awad Marius Lindauer Frank Hutter 201 104 0 14 Sep 2021
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspective Kaixuan Huang Yuqing Wang Molei Tao T. Zhao MLT 51 98 0 14 Feb 2020
NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search Xuanyi Dong Yi Yang 141 714 0 02 Jan 2020
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization Aaron Klein Frank Hutter 41 93 0 13 May 2019
Meta-Learning: A Survey Joaquin Vanschoren FedML OOD 70 762 0 08 Oct 2018
Probabilistic Model-Agnostic Meta-Learning Chelsea Finn Kelvin Xu Sergey Levine BDL 278 672 0 07 Jun 2018
Maximizing acquisition functions for Bayesian optimization James T. Wilson Frank Hutter M. Deisenroth 129 247 0 25 May 2018
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets P. Chrabaszcz I. Loshchilov Frank Hutter SSeg OOD 170 649 0 27 Jul 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 829 11,943 0 09 Mar 2017
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization A. Marco Felix Berkenkamp Philipp Hennig Angela P. Schoellig Andreas Krause S. Schaal Sebastian Trimpe 72 128 0 03 Mar 2017
Learning to learn by gradient descent by gradient descent Marcin Andrychowicz Misha Denil Sergio Gomez Colmenarejo Matthew W. Hoffman David Pfau Tom Schaul Brendan Shillingford Nando de Freitas 124 2,008 0 14 Jun 2016
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Djork-Arné Clevert Thomas Unterthiner Sepp Hochreiter 305 5,534 0 23 Nov 2015
Bayesian optimization for materials design P. Frazier Jialei Wang AI4CE 62 228 0 03 Jun 2015
Weight Uncertainty in Neural Networks Charles Blundell Julien Cornebise Koray Kavukcuoglu Daan Wierstra UQCV BDL 192 1,892 0 20 May 2015
OpenML: networked science in machine learning Joaquin Vanschoren Jan N. van Rijn B. Bischl Luís Torgo FedML AI4CE 167 1,327 0 29 Jul 2014
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions José Miguel Hernández-Lobato Matthew W. Hoffman Zoubin Ghahramani 107 647 0 10 Jun 2014
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek Hugo Larochelle Ryan P. Adams 373 7,957 0 13 Jun 2012
Entropy Search for Information-Efficient Global Optimization Philipp Hennig Christian J. Schuler 123 673 0 06 Dec 2011
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 174 4,313 0 18 Nov 2011