Freeze-Thaw Bayesian Optimization

16 June 2014

Papers citing "Freeze-Thaw Bayesian Optimization"

50 / 54 papers shown

Title
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure Jihao Andreas Lin Sebastian Ament Maximilian Balandat E. Bakshy BDL 29 2 0 11 Oct 2024
Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection Matteo Zecchin Sangwoo Park Osvaldo Simeone LM&MA 77 3 0 24 Sep 2024
A Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offs Wenyu Wang Zheyi Fan Szu Hui Ng 33 0 0 24 May 2024
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization Herilalaina Rakotoarison Steven Adriaensen Neeratyoy Mallik Samir Garibov Eddie Bergman Frank Hutter AI4CE 40 9 0 25 Apr 2024
NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks Fergal Stapleton Edgar Galván López 48 2 0 12 Apr 2024
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks Steven Adriaensen Herilalaina Rakotoarison Samuel G. Müller Frank Hutter BDL 31 19 0 31 Oct 2023
Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems Songjie Yang Baojuan Liu Zhiqin Hong Zhong-pei Zhang 12 8 0 28 Jul 2022
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach Syrine Belakaria J. Doppa Nicolò Fusi Rishit Sheth 33 7 0 25 Jun 2022
A Probabilistic Machine Learning Approach to Scheduling Parallel Loops with Bayesian Optimization Kyurae Kim Youngjae Kim Sungyong Park 23 12 0 12 Jun 2022
FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization Zhen Wang Weirui Kuang Ce Zhang Bolin Ding Yaliang Li FedML 31 13 0 08 Jun 2022
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning Yang Li Yu Shen Huaijun Jiang Wentao Zhang Zhi-Xin Yang Ce Zhang Bin Cui 36 15 0 06 Jun 2022
Amortized Proximal Optimization Juhan Bae Paul Vicol Jeff Z. HaoChen Roger C. Grosse ODL 27 14 0 28 Feb 2022
Learning Curves for Decision Making in Supervised Machine Learning: A Survey F. Mohr Jan N. van Rijn 41 53 0 28 Jan 2022
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies Paul Vicol Luke Metz Jascha Narain Sohl-Dickstein 27 67 0 27 Dec 2021
Automated Deep Learning: Neural Architecture Search Is Not the End Xuanyi Dong D. Kedziora Katarzyna Musial Bogdan Gabrys 25 26 0 16 Dec 2021
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation Ross M. Clarke E. T. Oldewage José Miguel Hernández-Lobato 28 9 0 20 Oct 2021
Non-smooth Bayesian Optimization in Tuning Problems Hengrui Luo J. Demmel Younghyun Cho Xin Li Yang Liu 25 13 0 15 Sep 2021
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 46 100 0 14 Sep 2021
RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving Ruochen Wang Xiangning Chen Minhao Cheng Xiaocheng Tang Cho-Jui Hsieh 41 13 0 18 Aug 2021
Accelerating Evolutionary Neural Architecture Search via Multi-Fidelity Evaluation Shangshang Yang Ye Tian Xiaoshu Xiang Shichen Peng Xing-yi Zhang 29 20 0 10 Aug 2021
HyperJump: Accelerating HyperBand via Risk Modelling Pedro Mendes Maria Casimiro Paolo Romano David Garlan 22 7 0 05 Aug 2021
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges B. Bischl Martin Binder Michel Lang Tobias Pielok Jakob Richter ... Theresa Ullmann Marc Becker A. Boulesteix Difan Deng Marius Lindauer 85 455 0 13 Jul 2021
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure Samuel Kim Peter Y. Lu Charlotte Loh Jamie Smith Jasper Snoek M. Soljavcić BDL AI4CE 124 17 0 23 Apr 2021
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization Valerio Perrone Huibin Shen Aida Zolic I. Shcherbatyi Amr Ahmed ... Barbara Pogorzelska Miroslav Miladinovic K. Kenthapadi Matthias Seeger Cédric Archambeau 45 16 0 15 Dec 2020
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians Juhan Bae Roger C. Grosse 27 24 0 26 Oct 2020
A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms Yizhou Sun Xian Sun Yuhan Fang Gary G. Yen 11 41 0 30 Aug 2020
Network Architecture Search for Domain Adaptation Yichen Li Xingchao Peng OOD 21 15 0 13 Aug 2020
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning Matthias Feurer Katharina Eggensperger Stefan Falkner Marius Lindauer Frank Hutter 35 266 0 08 Jul 2020
A Semi-Supervised Assessor of Neural Architectures Yehui Tang Yunhe Wang Yixing Xu Hanting Chen Chunjing Xu Boxin Shi Chao Xu Qi Tian Chang Xu 16 67 0 14 May 2020
Predicting Neural Network Accuracy from Weights Thomas Unterthiner Daniel Keysers Sylvain Gelly Olivier Bousquet Ilya O. Tolstikhin 30 101 0 26 Feb 2020
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits Jack Parker-Holder Vu Nguyen Stephen J. Roberts OffRL 75 83 0 06 Feb 2020
BISTRO: Berkeley Integrated System for Transportation Optimization Sidney A. Feygin Jessica R. Lazarus E. Forscher Valentine Golfier-Vetterli Jonathan W. Lee Abhishek Gupta Rashid A. Waraich C. Sheppard Alexandre M. Bayen 19 8 0 10 Aug 2019
Towards AutoML in the presence of Drift: first results Jorge G. Madrid Hugo Jair Escalante E. Morales Wei-Wei Tu Yang Yu Lisheng Sun-Hosoya Isabelle M Guyon Michele Sebag 11 25 0 24 Jul 2019
Techniques for Automated Machine Learning Yi-Wei Chen Qingquan Song Xia Hu 18 48 0 21 Jul 2019
Benchmark and Survey of Automated Machine Learning Frameworks Marc-André Zöller Marco F. Huber 25 86 0 26 Apr 2019
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly Kirthevasan Kandasamy Karun Raju Vysyaraju Willie Neiswanger Biswajit Paria Christopher R. Collins J. Schneider Barnabás Póczós Eric Xing 29 174 0 15 Mar 2019
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions M. Mackay Paul Vicol Jonathan Lorraine David Duvenaud Roger C. Grosse 27 164 0 07 Mar 2019
Fast Efficient Hyperparameter Tuning for Policy Gradients Supratik Paul Vitaly Kurin Shimon Whiteson 22 32 0 18 Feb 2019
A System for Massively Parallel Hyperparameter Tuning Liam Li Kevin G. Jamieson Afshin Rostamizadeh Ekaterina Gonina Moritz Hardt Benjamin Recht Ameet Talwalkar 24 372 0 13 Oct 2018
Meta-Learning: A Survey Joaquin Vanschoren FedML OOD 34 756 0 08 Oct 2018
Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks Tobias Hinz Nicolás Navarro-Guerrero S. Magg S. Wermter 27 104 0 19 Jul 2018
A Tutorial on Bayesian Optimization P. Frazier GP 15 1,737 0 08 Jul 2018
TAPAS: Train-less Accuracy Predictor for Architecture Search R. Istrate F. Scheidegger G. Mariani Dimitrios S. Nikolopoulos C. Bekas A. Malossi OOD 32 75 0 01 Jun 2018
Stochastic Hyperparameter Optimization through Hypernetworks Jonathan Lorraine David Duvenaud 41 139 0 26 Feb 2018
Accelerating Neural Architecture Search using Performance Prediction Bowen Baker O. Gupta Ramesh Raskar Nikhil Naik 15 30 0 30 May 2017
Bayesian Optimization with Shape Constraints Michael Jauch Víctor Pena 14 11 0 28 Dec 2016
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes J. Herten Ivo Couckuyt T. Dhaene GP 11 1 0 01 Dec 2016
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates Ilija Ilievski Taimoor Akhtar Jiashi Feng C. Shoemaker 26 150 0 28 Jul 2016
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets Aaron Klein Stefan Falkner Simon Bartels Philipp Hennig Frank Hutter AI4CE 35 545 0 23 May 2016
Bayesian Hyperparameter Optimization for Ensemble Learning Julien-Charles Levesque Christian Gagné R. Sabourin BDL 21 48 0 20 May 2016