William La Cava — accepted AI-conference papers

2024

Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias

Shan Chen, Jack Gallifant, Mingye Gao, Pedro José Ferreira Moreira, Nikolaj Munch, Ajay Muthukkumar +9

NeurIPS 2024poster

Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in their training data. In this study, we introduce \textbf{Cross-Care}, the first benchmark framework dedicated to assessin…

Cited by 6 Source ↗PDF ↗code ↗Scholar ↗

2021

Contemporary Symbolic Regression Methods and their Relative Performance

William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabricio Olivetti de Franca, Marco Virgolin, Ying Jin +2

NeurIPS 2021poster

Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent benchmarking standards. In this paper, we address this shortcoming by introducing an open-source, reproducible benchmark…

Cited by 336 Source ↗Scholar ↗

2019

Learning concise representations for regression by evolving networks of trees

William La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore

ICLR 2019poster

We propose and study a method for learning interpretable representations for the task of regression. Features are represented as networks of multi-type expression trees comprised of activation functions common in neural networks in addition to other elementary functions. Differentiable features are…

Cited by 75 Source ↗PDF ↗Scholar ↗