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2201.12909
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Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
International Conference on Machine Learning (ICML), 2022
30 January 2022
Daniele Calandriello
Luigi Carratino
A. Lazaric
Michal Valko
Lorenzo Rosasco
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Papers citing
"Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times"
17 / 17 papers shown
Quantum Bayesian Optimization for Quality Improvement in Fuselage Assembly
Jiayu Liu
Chong Liu
Trevor Rhone
Y. Wang
61
0
0
27 Nov 2025
No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes
Jasmine Bayrooti
Sattar Vakili
Amanda Prorok
Carl Henrik Ek
151
2
0
23 Oct 2025
Advancing Knowledge Tracing by Exploring Follow-up Performance Trends
Hengyu Liu
Yushuai Li
Minghe Yu
Tiancheng Zhang
Ge Yu
Torben Bach Pedersen
Kristian Torp
Christian S. Jensen
Tianyi Li
AI4Ed
400
0
0
11 Aug 2025
Gradient-based Sample Selection for Faster Bayesian Optimization
Qiyu Wei
Haowei Wang
Zirui Cao
Songhao Wang
Richard Allmendinger
Mauricio A Álvarez
357
1
0
10 Apr 2025
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
Neural Information Processing Systems (NeurIPS), 2024
Sattar Vakili
Julia Olkhovskaya
348
3
0
30 Oct 2024
The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs
Qiyuan Chen
Raed Al Kontar
338
1
0
18 Oct 2024
Quality with Just Enough Diversity in Evolutionary Policy Search
Paul Templier
Luca Grillotti
Emmanuel Rachelson
Dennis G. Wilson
Antoine Cully
243
4
0
07 May 2024
Constraint-Guided Online Data Selection for Scalable Data-Driven Safety Filters in Uncertain Robotic Systems
IEEE Transactions on robotics (TRO), 2023
Jason J. Choi
F. Castañeda
Wonsuhk Jung
Bike Zhang
Claire J. Tomlin
Koushil Sreenath
300
5
0
23 Nov 2023
Batch Bayesian Optimization for Replicable Experimental Design
Neural Information Processing Systems (NeurIPS), 2023
Zhongxiang Dai
Q. Nguyen
Sebastian Shenghong Tay
Daisuke Urano
Richalynn Leong
Bryan Kian Hsiang Low
Patrick Jaillet
268
7
0
02 Nov 2023
Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization
International Conference on Learning Representations (ICLR), 2023
I. Gemp
Luke Marris
Georgios Piliouras
456
14
0
10 Oct 2023
Quantum Bayesian Optimization
Neural Information Processing Systems (NeurIPS), 2023
Zhongxiang Dai
Gregory Kang Ruey Lau
Arun Verma
Yao Shu
K. H. Low
Patrick Jaillet
309
18
0
09 Oct 2023
(Private) Kernelized Bandits with Distributed Biased Feedback
Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS), 2023
Fengjiao Li
Xingyu Zhou
Bo Ji
469
6
0
28 Jan 2023
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems
Neural Information Processing Systems (NeurIPS), 2022
Shyam Sundhar Ramesh
Pier Giuseppe Sessa
Andreas Krause
Ilija Bogunovic
225
14
0
14 Oct 2022
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
International Conference on Machine Learning (ICML), 2022
Sattar Vakili
Jonathan Scarlett
Da-shan Shiu
A. Bernacchia
397
22
0
08 Feb 2022
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits
Neural Information Processing Systems (NeurIPS), 2022
Ilija Bogunovic
Zihan Li
Andreas Krause
Jonathan Scarlett
335
11
0
03 Feb 2022
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization
Marco Rando
Luigi Carratino
S. Villa
Lorenzo Rosasco
482
8
0
16 Jun 2021
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
Yuta Saito
Shunsuke Aihara
Megumi Matsutani
Yusuke Narita
OffRL
737
93
0
17 Aug 2020
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