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Found 8 papers, 4 papers with code
Myopically Verifiable Probabilistic Certificates for Safe Control and Learning
When the target probability is defined using long-term trajectories, this technique can be used to design myopic conditions/controllers with assured long-term safe probability.
Physics-informed RL for Maximal Safety Probability Estimation
Motivated by this, we study how to estimate the long-term safety probability of maximally safe actions without sufficient coverage of samples from risky states and long-term trajectories.
An Analytic Solution to Covariance Propagation in Neural Networks
Uncertainty quantification of neural networks is critical to measuring the reliability and robustness of deep learning systems.
Context-aware LLM-based Safe Control Against Latent Risks
It is challenging for autonomous control systems to perform complex tasks in the presence of latent risks.
Sample-Optimal Zero-Violation Safety For Continuous Control
In this paper, we study the problem of ensuring safety with a few shots of samples for partially unknown systems.
Physics-Informed Representation and Learning: Control and Risk Quantification
Optimal and safety-critical control are fundamental problems for stochastic systems, and are widely considered in real-world scenarios such as robotic manipulation and autonomous driving.
A Generalizable Physics-informed Learning Framework for Risk Probability Estimation
In this paper, we develop an efficient method to evaluate the probabilities of long-term risk and their gradients.
A Learning and Control Perspective for Microfinance
Although many methods in regular finance can estimate credit scores and default probabilities, these methods are not directly applicable to microfinance due to the following unique characteristics: a) under-explored (developing) areas such as rural Africa do not have sufficient prior loan data for microfinance institutions (MFIs) to establish a credit scoring system; b) microfinance applicants may have difficulty providing sufficient information for MFIs to accurately predict default probabilities; and c) many MFIs use group liability (instead of collateral) to secure repayment.
