arXiv Machine Learning June 29, 2026 By Domagoj Herceg

PAC-Bayesian Certificates for Quadratic Closed-Loop Control

Read the original on arXiv Machine Learning →

arXiv:2606. 28281v1 Announce Type: cross Abstract: PAC-Bayesian bounds provide finite-sample guarantees for data-dependent randomized predictors, but applying them to learning-based control is difficult because the natural objective is a quadratic trajectory cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 4

Robust Bayesian Optimization via Tempered Posteriors

arXiv:2601. 07094v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) iteratively fits a Gaussian process (GP) surrogate to accumulated evaluations and selects new queries via an acquisition function.

By Jiguang Li, Hengrui Luo

arXiv Machine Learning

Sep 1

Generalization as a robust performance property of learning-enabled dynamical systems

arXiv:2608.30431v1 Announce Type: cross Abstract: By focusing on algorithmic stability as a means of establishing out-of-sample bounds, we provide a system-theoretic interpretation of generalization...

By Filippo Fabiani

arXiv Machine Learning

5d ago

Learning Chance-Constrained MDPs with Bellman Distributional Certificates

The paper introduces a new approach to learning chance-constrained Markov decision processes (CCMDPs) using a Bellman distributional certificate. It provides both model-based and model-free algorithms with theoretical guarantees, including matching upper and lower bounds for tabular discounted CCMDPs with bounded successor support. Numerical experiments on synthetic CCMDPs and an IEEE 14-bus energy storage benchmark demonstrate the safety and effectiveness of the proposed methods.

By Chenbei Lu, Hongyu Yi

reinforcement-learning benchmarks safety

arXiv Machine Learning

Sep 11

Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization

arXiv:2605. 20145v2 Announce Type: replace-cross Abstract: Gaussian process (GP) predictive distributions are commonly used in Bayesian optimization (BO) to guide the selection of evaluation points for expensive objective functions.

By Aur\'elien Pion, Emmanuel Vazquez

benchmarks

arXiv Machine Learning

Jun 24

Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces

arXiv:2603. 08287v2 Announce Type: replace-cross Abstract: We analyze the Bayesian regret of the Gaussian process posterior sampling reinforcement learning (GP-PSRL) algorithm.

By Hamish Flynn, Joe Watson, Ingmar Posner, Jan Peters

reinforcement-learning

arXiv Machine Learning

Aug 12

Path Integral Value Matching for Linear Quadratic Stochastic Optimal Control

arXiv:2608. 10777v1 Announce Type: new Abstract: Linear Quadratic Stochastic Optimal Control (LQ-SOC) establishes a fundamental framework for steering noisy dynamical systems and has recently gained renewed interest in the machine learning community.

By Bangyan Liao, Chenglei Yu, Yuchen Yang, Chuanrui Wang, Zhisheng Song, Peidong Liu, Tailin Wu

benchmarks

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Robust Bayesian Optimization via Tempered Posteriors

Generalization as a robust performance property of learning-enabled dynamical systems

Learning Chance-Constrained MDPs with Bellman Distributional Certificates

Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization

Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces

Path Integral Value Matching for Linear Quadratic Stochastic Optimal Control