Jim Weatherall — accepted AI-conference papers

2026

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback

Evgeny S. Saveliev, Samuel Holt, Nabeel Seedat, David Bentley, Jim Weatherall, Mihaela van der Schaar

ICML 2026poster

Large Language Models (LLMs) offer a promising avenue for scientific discovery, yet their application to symbolic regression is often constrained by inefficient search strategies and coarse feedback signals. Current methods typically guide LLMs using scalar metrics (e.g., global Mean Squared Error),…

Cited by 0 Source ↗Scholar ↗

2024

Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models

Samuel Holt, Zhaozhi Qian, Tennison Liu, Jim Weatherall, Mihaela van der Schaar

NeurIPS 2024poster

The discovery of dynamical systems is crucial across a range of fields, including pharmacology, epidemiology, and physical sciences. *Accurate* and *interpretable* modeling of these systems is essential for understanding complex temporal processes, optimizing interventions, and minimizing adverse ef…

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2021

Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation

Alicia Curth, David Svensson, Jim Weatherall, Mihaela van der Schaar

NeurIPS 2021poster

The machine learning (ML) toolbox for estimation of heterogeneous treatment effects from observational data is expanding rapidly, yet many of its algorithms have been evaluated only on a very limited set of semi-synthetic benchmark datasets. In this paper, we investigate current benchmarking practic…

Cited by 87 Source ↗Scholar ↗