5 papers
Fact-Augmented Lookahead Planning for LLM Agents
Samuel Holt, Max Ruiz Luyten, Thomas Pouplin +1
Large Language Models (LLMs) are increasingly capable, but LLM agents still struggle to plan effectively in interactive, partially observable, long-horizon environments when search…
The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data
Thomas Pouplin, Katarzyna Kobalczyk, Hao Sun +1
Developing autonomous agents capable of performing complex, multi-step decision-making tasks specified in natural language remains a significant challenge, particularly in realisti…
CliMB: An AI-enabled Partner for Clinical Predictive Modeling
Evgeny Saveliev, Tim Schubert, Thomas Pouplin +2
Despite its significant promise and continuous technical advances, real-world applications of artificial intelligence (AI) remain limited. We attribute this to the "domain expert-A…
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise
Thomas Pouplin, Alan Jeffares, Nabeel Seedat +1
Constructing valid prediction intervals rather than point estimates is a well-established approach for uncertainty quantification in the regression setting. Models equipped with th…
Retrieval Augmented Thought Process for Private Data Handling in Healthcare
Thomas Pouplin, Hao Sun, Samuel Holt +1
Large Language Models (LLMs) have demonstrated the strong potential to assist both clinicians and the general public with their extensive medical knowledge. However, their applicat…