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Leonardo Cotta

5 accepted papers

2025

Measuring Scientific Capabilities of Language Models with a Systems Biology Dry Lab

Haonan Duan, Stephen Zhewen Lu, Caitlin Fiona Harrigan, Nishkrit Desai, Jiarui Lu, Michał Koziarski +2
NeurIPS 2025poster

Designing experiments and result interpretations are core scientific competencies, particularly in biology, where researchers perturb complex systems to uncover the underlying systems. Recent efforts to evaluate the scientific capabilities of large language models (LLMs) fail to test these competenc…

Cited by 0Source ↗Scholar ↗
2024

End-To-End Causal Effect Estimation from Unstructured Natural Language Data

Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul Krishnan, Chris J. Maddison
NeurIPS 2024poster

Knowing the effect of an intervention is critical for human decision-making, but current approaches for causal effect estimation rely on manual data collection and structuring, regardless of the causal assumptions. This increases both the cost and time-to-completion for studies. We show how large, d…

Cited by 9Source ↗PDF ↗Scholar ↗
2023

Probabilistic Invariant Learning with Randomized Linear Classifiers

Leonardo Cotta, Gal Yehuda, Assaf Schuster, Chris J. Maddison
NeurIPS 2023poster

Designing models that are both expressive and preserve known invariances of tasks is an increasingly hard problem. Existing solutions tradeoff invariance for computational or memory resources. In this work, we show how to leverage randomness and design models that are both expressive and invariant b…

Cited by 2Source ↗PDF ↗Scholar ↗