1 citations · 1 across the 4 of their papers we have counts for
5 papers
CausalXRL: Explainable Reinforcement Learning through Causal Graph Reasoning
Yanming Zhang, Eric Papenhausen, Klaus Mueller
Reinforcement learning is a powerful paradigm for training autonomous agents and has achieved impressive performance in complex environments. However, this success often comes at t…
CausalChat: Interactive Causal Model Development and Refinement Using Large Language Models
Yanming Zhang, Akshith Kota, Eric Papenhausen +1
Causal networks are widely used in many fields to model the complex relationships between variables. A recent approach has sought to construct causal networks by leveraging the wis…
An Explainable AI Approach to Large Language Model Assisted Causal Model Auditing and Development
Yanming Zhang, Brette Fitzgibbon, Dino Garofolo +3
Causal networks are widely used in many fields, including epidemiology, social science, medicine, and engineering, to model the complex relationships between variables. While it ca…
Patterns of Social Vulnerability -- An Interactive Dashboard to Explore Risks to Public Health on the US County Level
Darius Coelho, Nikita Gupta, Eric Papenhausen +1
Social vulnerability is the susceptibility of a community to be adversely impacted by natural hazards and public health emergencies, such as drought, earthquakes, flooding, virus o…
Optimizing the domain wall fermion Dirac operator using the R-Stream source-to-source compiler
Meifeng Lin, Eric Papenhausen, M. Harper Langston +4
The application of the Dirac operator on a spinor field, the Dslash operation, is the most computation-intensive part of the lattice QCD simulations. It is often the key kernel to…