Hierarchical Neuro-Symbolic Decision Transformer.

Risk-Averse Reinforcement Learning: An Optimal Transport Perspective on Temporal Difference Learning.

Multi-Scale Conformal Prediction: A Theoretical Framework with Coverage Guarantees.

PEARL: Preconditioner Enhancement through Actor-critic Reinforcement Learning.

Wasserstein Adaptive Value Estimation for Actor-Critic Reinforcement Learning.

SMTL: A Stratified Logic for Expressive Multi-Level Temporal Specifications.

A survey on reinforcement learning in aviation applications.

Hierarchical Upper Confidence Bounds for Constrained Online Learning.

Optimizing Falsification for Learning-Based Control Systems: A Multi-Fidelity Bayesian Approach.

Optimal Transport-Assisted Risk-Sensitive Q-Learning.

Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning.

The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning.

BEACON: A Bayesian Evolutionary Approach for Counterexample Generation of Control Systems.

Exploring the role of simulator fidelity in the safety validation of learning-enabled autonomous systems.

LLMs-augmented Contextual Bandit.

Understanding Reward Ambiguity Through Optimal Transport Theory in Inverse Reinforcement Learning.

Risk-Aware Reinforcement Learning through Optimal Transport Theory.

Towards Theoretical Understanding of Data-Driven Policy Refinement.

Joint Falsification and Fidelity Settings Optimization for Validation of Safety-Critical Systems: A Theoretical Analysis.

Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learning.

Fast variable selection makes scalable Gaussian process BSS-ANOVA a speedy and accurate choice for tabular and time series regression.

A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems.

A Framework for Controlling Multi-Robot Systems Using Bayesian Optimization and Linear Combination of Vectors.

Safety Verification of Autonomous Systems: A Multi-Fidelity Reinforcement Learning Approach.

Safe Reinforcement Learning with Mixture Density Network: A Case Study in Autonomous Highway Driving.

Spatiotemporal Optimization Through Gaussian Process-Based Model Predictive Control: A Case Study in Airborne Wind Energy.

Economically Efficient Combined Plant and Controller Design Using Batch Bayesian Optimization: Mathematical Framework and Airborne Wind Energy Case Study.