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Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control

4 June 2019

Papers citing "Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control"

50 / 61 papers shown

Title
Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems Armin Lederer Erfaun Noorani Andreas Krause 13 0 0 09 Jun 2025
Optimal kernel regression bounds under energy-bounded noise Amon Lahr Johannes Köhler Anna Scampicchio Melanie Zeilinger 34 0 0 28 May 2025
Convergence Rates of Constrained Expected Improvement Haowei Wang Jingyi Wang Zhongxiang Dai Nai-Yuan Chiang Szu Hui Ng Cosmin G. Petra 53 0 0 16 May 2025
Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics Manish Prajapat Johannes Köhler Amon Lahr Andreas Krause Melanie Zeilinger 204 0 0 12 May 2025
The Hardness of Validating Observational Studies with Experimental Data Jake Fawkes Michael O'Riordan Athanasios Vlontzos Oriol Corcoll Ciarán M. Gilligan-Lee 81 2 0 19 Mar 2025
Gearing Gaussian process modeling and sequential design towards stochastic simulators M. Binois A. Fadikar Abby Stevens 155 0 0 10 Dec 2024
Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective Raed Al Kontar FedML 100 1 0 12 Nov 2024
Towards safe Bayesian optimization with Wiener kernel regression O. Molodchyk Johannes Teutsch T. Faulwasser 62 1 0 04 Nov 2024
Decision-Point Guided Safe Policy Improvement Abhishek Sharma Leo Benac S. Parbhoo Finale Doshi-Velez OffRL 70 1 0 12 Oct 2024
Error Bounds For Gaussian Process Regression Under Bounded Support Noise With Applications To Safety Certification Robert Reed Luca Laurenti Morteza Lahijanian 42 4 0 16 Aug 2024
Quantifying Local Model Validity using Active Learning Sven Lämmle Can Bogoclu Robert Voßhall Anselm Haselhoff Dirk Roos 68 0 0 11 Jun 2024
Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization Zheyi Fan Wenyu Wang Szu Hui Ng Q. Hu 54 2 0 24 May 2024
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes Samuel Tesfazgi Markus Kessler Emilio Trigili Armin Lederer Sandra Hirche 25 0 0 14 May 2024
On Safety in Safe Bayesian Optimization Christian Fiedler Johanna Menn Lukas Kreisköther Sebastian Trimpe 79 11 0 19 Mar 2024
Stopping Bayesian Optimization with Probabilistic Regret Bounds James T. Wilson 65 4 0 26 Feb 2024
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression X. Dai Zewen Yang Mengtian Xu Fangzhou Liu Georges Hattab Sandra Hirche 52 3 0 05 Feb 2024
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies Zewen Yang Songbo Dong Armin Lederer X. Dai Siyu Chen Stefan Sosnowski Georges Hattab Sandra Hirche 40 6 0 05 Feb 2024
Whom to Trust? Elective Learning for Distributed Gaussian Process Regression Zewen Yang X. Dai Akshat Dubey Sandra Hirche Georges Hattab 34 10 0 05 Feb 2024
Modeling and Predicting Epidemic Spread: A Gaussian Process Regression Approach B. She Lei Xin Philip E. Paré Matthew T. Hale 28 0 0 14 Dec 2023
Towards Safe Multi-Task Bayesian Optimization Jannis O. Lübsen Christian Hespe Annika Eichler 53 3 0 12 Dec 2023
Constraint-Guided Online Data Selection for Scalable Data-Driven Safety Filters in Uncertain Robotic Systems Jason J. Choi F. Castañeda Wonsuhk Jung Bike Zhang Claire J. Tomlin Koushil Sreenath 92 3 0 23 Nov 2023
Promises of Deep Kernel Learning for Control Synthesis Robert Reed Luca Laurenti Morteza Lahijanian BDL 52 5 0 12 Sep 2023
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy Zhenxiao Yin X. Dai Zewen Yang Yang-Wu Shen Georges Hattab Haiying Zhao 51 10 0 26 Jul 2023
Episodic Gaussian Process-Based Learning Control with Vanishing Tracking Errors Armin Lederer Jonas Umlauft Sandra Hirche 76 0 0 10 Jul 2023
Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design Xubo Yue Raed Al Kontar A. Berahas Yang Liu Blake N. Johnson 46 4 0 25 Jun 2023
Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning X. Dai Armin Lederer Zewen Yang Sandra Hirche 98 13 0 14 May 2023
$Toward $L_\infty$-recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields$ Toward $L_\infty$ -recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields Kefan Dong Tengyu Ma 88 4 0 29 Apr 2023
Stochastic Cell Transmission Models of Traffic Networks Zachary Feinstein M. Kleiber Stefan Weber 40 1 0 23 Apr 2023
Error Bounds for Kernel-Based Linear System Identification with Unknown Hyperparameters Mingzhou Yin Roy S. Smith 54 0 0 17 Mar 2023
Safe Machine-Learning-supported Model Predictive Force and Motion Control in Robotics Janine Matschek Johanna Bethge R. Findeisen 65 9 0 08 Mar 2023
Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions Armin Lederer Azra Begzadić Neha Das Sandra Hirche 38 4 0 01 Dec 2022
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions Yikun Cheng Pan Zhao N. Hovakimyan OffRL 43 12 0 30 Nov 2022
Probabilistic Safeguard for Reinforcement Learning Using Safety Index Guided Gaussian Process Models Weiye Zhao Tairan He Changliu Liu 77 21 0 03 Oct 2022
Safe and Efficient Exploration of Human Models During Human-Robot Interaction Ravi Pandya Changliu Liu 46 6 0 01 Aug 2022
Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement Learning He-lu Huang Zerui Li Dongkun Han 71 2 0 16 Jun 2022
Scaling ResNets in the Large-depth Regime Pierre Marion Adeline Fermanian Gérard Biau Jean-Philippe Vert 105 16 0 14 Jun 2022
Power of Quantum Generative Learning Yuxuan Du Zhuozhuo Tu Bujiao Wu Xiao Yuan Dacheng Tao AI4CE 74 8 0 10 May 2022
Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes Armin Lederer Mingmin Zhang Samuel Tesfazgi Sandra Hirche 49 3 0 23 Feb 2022
Formal Verification of Unknown Dynamical Systems via Gaussian Process Regression John Jackson Luca Laurenti Eric Frew Morteza Lahijanian 66 16 0 31 Dec 2021
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators Jan Brüdigam Martin Schuck A. Capone Stefan Sosnowski Sandra Hirche 82 4 0 10 Dec 2021
Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes Alejandro Jose Ordóñez Conejo Armin Lederer Sandra Hirche 134 4 0 05 Nov 2021
Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty Shen Li Theodoros Stouraitis Michael Gienger S. Vijayakumar J. Shah 41 8 0 18 Oct 2021
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications A. Capone Armin Lederer Sandra Hirche 61 21 0 06 Sep 2021
A New Representation of Successor Features for Transfer across Dissimilar Environments Majid Abdolshah Hung Le Thommen George Karimpanal Sunil R. Gupta Santu Rana Svetha Venkatesh 58 18 0 18 Jul 2021
Uncertainty-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric Dawei Sun M. J. Khojasteh S. Shekhar Chuchu Fan 62 2 0 13 May 2021
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression Christian Fiedler C. Scherer Sebastian Trimpe GP 77 70 0 06 May 2021
Distributionally robust risk map for learning-based motion planning and control: A semidefinite programming approach A. Hakobyan Insoon Yang 167 25 0 03 May 2021
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations Wei W. Xing A. Shah Peng Wang Shandian Zhe Robert M. Kirby 61 12 0 08 Apr 2021
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control Armin Lederer Jonas Umlauft Sandra Hirche 63 20 0 13 Jan 2021
Control Barriers in Bayesian Learning of System Dynamics Vikas Dhiman M. J. Khojasteh M. Franceschetti Nikolay Atanasov 117 67 0 29 Dec 2020