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No-Regret Bayesian Optimization with Unknown Hyperparameters
v1v2 (latest)

No-Regret Bayesian Optimization with Unknown Hyperparameters

10 January 2019
Felix Berkenkamp
Angela P. Schoellig
Andreas Krause
    TPM
ArXiv (abs)PDFHTML

Papers citing "No-Regret Bayesian Optimization with Unknown Hyperparameters"

50 / 50 papers shown
Thompson Sampling via Fine-Tuning of LLMs
Thompson Sampling via Fine-Tuning of LLMs
Nicolas Menet
Aleksandar Terzić
Michael Hersche
Andreas Krause
Abbas Rahimi
250
0
0
15 Oct 2025
Online Learning and Coverage of Unknown Fields Using Random-Feature Gaussian Processes
Online Learning and Coverage of Unknown Fields Using Random-Feature Gaussian Processes
Ruijie Du
Ruoyu Lin
Yanning Shen
Magnus Egerstedt
189
0
0
09 Sep 2025
Learning Safe Control via On-the-Fly Bandit Exploration
Learning Safe Control via On-the-Fly Bandit Exploration
A. Capone
Ryan K. Cosner
Aaaron Ames
Sandra Hirche
324
0
0
12 Jun 2025
Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
Shogo Iwazaki
GP
350
9
0
02 Jun 2025
High-dimensional Nonparametric Contextual Bandit Problem
High-dimensional Nonparametric Contextual Bandit Problem
Shogo Iwazaki
Junpei Komiyama
Masaaki Imaizumi
256
1
0
20 May 2025
Just One Layer Norm Guarantees Stable Extrapolation
Just One Layer Norm Guarantees Stable Extrapolation
Juliusz Ziomek
George Whittle
Michael A. Osborne
364
3
0
20 May 2025
Wasserstein Barycenter Gaussian Process based Bayesian Optimization
Wasserstein Barycenter Gaussian Process based Bayesian Optimization
Antonio Candelieri
Andrea Ponti
Francesco Archetti
454
2
0
18 May 2025
Reliable algorithm selection for machine learning-guided design
Reliable algorithm selection for machine learning-guided design
Clara Fannjiang
Ji Won Park
348
2
0
26 Mar 2025
Safe exploration in reproducing kernel Hilbert spaces
Safe exploration in reproducing kernel Hilbert spacesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Abdullah Tokmak
Kiran G. Krishnan
Thomas B. Schon
Dominik Baumann
293
7
0
13 Mar 2025
Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz Constants
Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz ConstantsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Fares Fourati
Salma Kharrat
Vaneet Aggarwal
Mohamed-Slim Alouini
372
1
0
06 Feb 2025
Graph Agnostic Causal Bayesian Optimisation
Graph Agnostic Causal Bayesian Optimisation
Sumantrak Mukherjee
Mengyan Zhang
Seth Flaxman
Sebastian Vollmer
CML
363
2
0
05 Nov 2024
Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds
  Logarithmically Closer to Optimal
Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to OptimalNeural Information Processing Systems (NeurIPS), 2024
Juliusz Ziomek
Masaki Adachi
Michael A. Osborne
503
4
0
14 Oct 2024
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems
Wei-Ting Tang
Ankush Chakrabarty
J. Paulson
656
2
0
05 Jun 2024
BO4IO: A Bayesian optimization approach to inverse optimization with
  uncertainty quantification
BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification
Yen-An Lu
Wei-Shou Hu
J. Paulson
Qi Zhang
211
6
0
28 May 2024
No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian
  Processes
No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian ProcessesConference on Uncertainty in Artificial Intelligence (UAI), 2024
Minbiao Han
Fengxue Zhang
Yuxin Chen
347
4
0
14 May 2024
A Quadrature Approach for General-Purpose Batch Bayesian Optimization
  via Probabilistic Lifting
A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting
Masaki Adachi
Satoshi Hayakawa
Martin Jørgensen
Saad Hamid
Harald Oberhauser
Michael A. Osborne
GP
445
3
0
18 Apr 2024
On Safety in Safe Bayesian Optimization
On Safety in Safe Bayesian Optimization
Christian Fiedler
Johanna Menn
Lukas Kreisköther
Sebastian Trimpe
364
18
0
19 Mar 2024
Vanilla Bayesian Optimization Performs Great in High Dimensions
Vanilla Bayesian Optimization Performs Great in High Dimensions
Carl Hvarfner
E. Hellsten
Luigi Nardi
494
87
0
03 Feb 2024
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian
  Regret Bounds
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret BoundsInternational Conference on Machine Learning (ICML), 2023
Shion Takeno
Yu Inatsu
Masayuki Karasuyama
Ichiro Takeuchi
494
13
0
07 Nov 2023
Batch Bayesian Optimization for Replicable Experimental Design
Batch Bayesian Optimization for Replicable Experimental DesignNeural Information Processing Systems (NeurIPS), 2023
Zhongxiang Dai
Q. Nguyen
Sebastian Shenghong Tay
Daisuke Urano
Richalynn Leong
Bryan Kian Hsiang Low
Patrick Jaillet
266
7
0
02 Nov 2023
Efficiently Identifying Hotspots in a Spatially Varying Field with
  Multiple Robots
Efficiently Identifying Hotspots in a Spatially Varying Field with Multiple Robots
Varun Suryan
Erfaun Noorani
194
2
0
14 Sep 2023
Tuning Legged Locomotion Controllers via Safe Bayesian Optimization
Tuning Legged Locomotion Controllers via Safe Bayesian OptimizationConference on Robot Learning (CoRL), 2023
Daniel Widmer
Dong-oh Kang
Bhavya Sukhija
Jonas Hübotter
Andreas Krause
Stelian Coros
346
17
0
12 Jun 2023
Provably Efficient Bayesian Optimization with Unknown Gaussian Process
  Hyperparameter Estimation
Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation
Huong Ha
Vu-Linh Nguyen
Hung Tran-The
Hongyu Zhang
Xiuzhen Zhang
Anton Van Den Hengel
383
1
0
12 Jun 2023
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits
Adaptation to Misspecified Kernel Regularity in Kernelised BanditsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Yusha Liu
Aarti Singh
358
3
0
26 Apr 2023
Self-Correcting Bayesian Optimization through Bayesian Active Learning
Self-Correcting Bayesian Optimization through Bayesian Active LearningNeural Information Processing Systems (NeurIPS), 2023
Carl Hvarfner
E. Hellsten
Katharina Eggensperger
Luigi Nardi
GP
426
23
0
21 Apr 2023
Sharp Calibrated Gaussian Processes
Sharp Calibrated Gaussian ProcessesNeural Information Processing Systems (NeurIPS), 2023
A. Capone
Geoff Pleiss
Sandra Hirche
UQCV
461
4
0
23 Feb 2023
Are Random Decompositions all we need in High Dimensional Bayesian
  Optimisation?
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?International Conference on Machine Learning (ICML), 2023
Juliusz Ziomek
Haitham Bou-Ammar
217
37
0
30 Jan 2023
Safe and Adaptive Decision-Making for Optimization of Safety-Critical
  Systems: The ARTEO Algorithm
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm
Buse Sibel Korkmaz
Marta Zagórowska
Mehmet Mercangöz
247
3
0
10 Nov 2022
Lifelong Bandit Optimization: No Prior and No Regret
Lifelong Bandit Optimization: No Prior and No RegretConference on Uncertainty in Artificial Intelligence (UAI), 2022
Felix Schur
Parnian Kassraie
Jonas Rothfuss
Andreas Krause
411
3
0
27 Oct 2022
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic
  Reparameterization
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic ReparameterizationNeural Information Processing Systems (NeurIPS), 2022
Sam Daulton
Xingchen Wan
David Eriksson
Maximilian Balandat
Michael A. Osborne
E. Bakshy
253
60
0
18 Oct 2022
Adjusted Expected Improvement for Cumulative Regret Minimization in
  Noisy Bayesian Optimization
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization
Shouri Hu
Haowei Wang
Zhongxiang Dai
K. H. Low
Szu Hui Ng
325
10
0
10 May 2022
Tuning Particle Accelerators with Safety Constraints using Bayesian
  Optimization
Tuning Particle Accelerators with Safety Constraints using Bayesian OptimizationPhysical Review Accelerators and Beams (PRAB), 2022
Johannes Kirschner
Mojmír Mutný
Andreas Krause
J. C. D. Portugal
N. Hiller
J. Snuverink
330
17
0
26 Mar 2022
Regret Bounds for Expected Improvement Algorithms in Gaussian Process
  Bandit Optimization
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit OptimizationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Hung The Tran
Sunil R. Gupta
Santu Rana
Svetha Venkatesh
223
18
0
15 Mar 2022
Meta-Learning Hypothesis Spaces for Sequential Decision-making
Meta-Learning Hypothesis Spaces for Sequential Decision-makingInternational Conference on Machine Learning (ICML), 2022
Parnian Kassraie
Jonas Rothfuss
Andreas Krause
OffRL
528
6
0
01 Feb 2022
Scaling Gaussian Process Optimization by Evaluating a Few Unique
  Candidates Multiple Times
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple TimesInternational Conference on Machine Learning (ICML), 2022
Daniele Calandriello
Luigi Carratino
A. Lazaric
Michal Valko
Lorenzo Rosasco
203
19
0
30 Jan 2022
GoSafeOpt: Scalable Safe Exploration for Global Optimization of
  Dynamical Systems
GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical SystemsArtificial Intelligence (AIJ), 2022
Bhavya Sukhija
M. Turchetta
David Lindner
Andreas Krause
Sebastian Trimpe
Dominik Baumann
426
24
0
24 Jan 2022
Online Calibrated and Conformal Prediction Improves Bayesian
  Optimization
Online Calibrated and Conformal Prediction Improves Bayesian Optimization
Shachi Deshpande
Charles Marx
Volodymyr Kuleshov
405
12
0
08 Dec 2021
Misspecified Gaussian Process Bandit Optimization
Misspecified Gaussian Process Bandit OptimizationNeural Information Processing Systems (NeurIPS), 2021
Ilija Bogunovic
Andreas Krause
272
58
0
09 Nov 2021
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian
  Optimization
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization
Tomoharu Iwata
BDL
192
5
0
01 Nov 2021
Pre-trained Gaussian processes for Bayesian optimization
Pre-trained Gaussian processes for Bayesian optimization
Zehao Wang
George E. Dahl
Kevin Swersky
Chansoo Lee
Zachary Nado
Justin Gilmer
Jasper Snoek
Zoubin Ghahramani
402
80
0
16 Sep 2021
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for
  Safety-Critical Applications
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical ApplicationsInternational Conference on Machine Learning (ICML), 2021
A. Capone
Armin Lederer
Sandra Hirche
250
26
0
06 Sep 2021
Additive Tree-Structured Conditional Parameter Spaces in Bayesian
  Optimization: A Novel Covariance Function and a Fast Implementation
Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation
Xingchen Ma
Matthew B. Blaschko
193
7
0
06 Oct 2020
Green Machine Learning via Augmented Gaussian Processes and
  Multi-Information Source Optimization
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization
Antonio Candelieri
R. Perego
Francesco Archetti
232
21
0
25 Jun 2020
Efficient Model-Based Reinforcement Learning through Optimistic Policy
  Search and Planning
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
Sebastian Curi
Felix Berkenkamp
Andreas Krause
475
111
0
15 Jun 2020
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement
  Learning
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement LearningTransportation Research Part C: Emerging Technologies (Transp. Res. Part C), 2020
Zhenyu Shou
Xuan Di
263
70
0
17 Feb 2020
Convergence Guarantees for Gaussian Process Means With Misspecified
  Likelihoods and Smoothness
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and SmoothnessJournal of machine learning research (JMLR), 2020
George Wynne
F. Briol
Mark Girolami
480
73
0
29 Jan 2020
Learning to Optimize Computational Resources: Frugal Training with
  Generalization Guarantees
Learning to Optimize Computational Resources: Frugal Training with Generalization GuaranteesAAAI Conference on Artificial Intelligence (AAAI), 2019
Maria-Florina Balcan
Tuomas Sandholm
Ellen Vitercik
353
17
0
26 May 2019
Efficient Batch Black-box Optimization with Deterministic Regret Bounds
Efficient Batch Black-box Optimization with Deterministic Regret Bounds
Yueming Lyu
Yuan. Yuan
Ivor W. Tsang
344
14
0
24 May 2019
Sampling Acquisition Functions for Batch Bayesian Optimization
Sampling Acquisition Functions for Batch Bayesian Optimization
Alessandro De Palma
Celestine Mendler-Dünner
Thomas Parnell
Andreea Anghel
H. Pozidis
200
15
0
22 Mar 2019
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and
  Adapting
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
A. Krishnamurthy
John Langford
Aleksandrs Slivkins
Chicheng Zhang
OffRL
477
72
0
05 Feb 2019
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