uncertainty quantification
4 notes, newest first.
Generalized Kalman filter based temporal difference reinforcement learning
The paper introduces a new reinforcement learning framework that combines conditional expectation theory with a generalized Kalman filter. This approach allows for the estimation of both the value function and its…
Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification
Researchers developed a machine learning model that can classify transient candidates in time-domain surveys, known as Real-Bogus classification, without needing human-labeled data. The model is trained using simulated…
UA-ChatDev: Uncertainty-Aware Multi-Agent Collaboration for Reliable Software Development
The researchers proposed UA-ChatDev, an uncertainty-aware multi-agent software development framework to improve the reliability of autonomous multi-agent software development. The framework uses a lightweight…
Bayesian control for coding agents
The paper proposes a Bayesian controller for coding agents that make decisions based on a belief over candidate correctness, rather than fixed rules. This method is found to be most useful when verification is costly…
