William G. La Cava

According to our database, William G. La Cava authored at least 50 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming.

Nuno M. Rodrigues, João E. Batista, William G. La Cava, Leonardo Vanneschi, Sara Silva

SN Comput. Sci., January, 2024

Benchmarking mortality risk prediction from electrocardiograms.

Platon Lukyanenko, Joshua Mayourian, Mingxuan Liu, John K. Triedman, Sunil J. Ghelani, William G. La Cava

CoRR, 2024

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

Shan Chen, Jack Gallifant, Mingye Gao, Pedro Moreira, Nikolaj Munch, Ajay Muthukkumar, Arvind Rajan, Jaya Kolluri, Amelia Fiske, Janna Hastings, Hugo J. W. L. Aerts, Brian Anthony, Leo Anthony Celi, William G. La Cava, Danielle S. Bitterman

CoRR, 2024

Minimum variance threshold for epsilon-lexicase selection.

Guilherme Seidyo Imai Aldeia, Fabrício Olivetti de França, William G. La Cava

Proceedings of the Genetic and Evolutionary Computation Conference, 2024

Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing.

Guilherme Seidyo Imai Aldeia, Fabrício Olivetti de França, William G. La Cava

Proceedings of the Genetic and Evolutionary Computation Conference, 2024

2023

Translating intersectionality to fair machine learning in health sciences.

Elle Lett, William G. La Cava

Nat. Mac. Intell., May, 2023

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

Amelia L. M. Tan, Emily J. Getzen, Meghan R. Hutch, Zachary H. Strasser, Alba Gutiérrez-Sacristán, Trang T. Le, Arianna Dagliati, Michele Morris, David A. Hanauer, Bertrand Moal, Clara-Lea Bonzel, William Yuan, Lorenzo Chiudinelli, Priyam Das, Harrison G. Zhang, Bruce J. Aronow, Paul Avillach, Gabriel A. Brat, Tianxi Cai, Chuan Hong, William G. La Cava, He Hooi Will Loh, Yuan Luo, Shawn N. Murphy, Kee Yuan Hgiam, Gilbert S. Omenn, Lav P. Patel, Malarkodi J. Samayamuthu, Emily R. Shriver, Zahra Shakeri Hossein Abad, Byorn W. L. Tan, Shyam Visweswaran, Xuan Wang, Griffin M. Weber, Zongqi Xia, Bertrand Verdy, Qi Long, Danielle L. Mowery, John H. Holmes

J. Biomed. Informatics, March, 2023

A flexible symbolic regression method for constructing interpretable clinical prediction models.

William G. La Cava, Paul C. Lee, Imran Ajmal, Xiruo Ding, Priyanka Solanki, Jordana B. Cohen, Jason H. Moore, Daniel S. Herman

npj Digit. Medicine, 2023

Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition.

Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, Maimuna S. Majumder, Miles D. Cranmer, Guilherme Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, Brenden K. Petersen, Ruben Glatt, T. Nathan Mundhenk, C. S. Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, A. Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava

CoRR, 2023

Optimizing fairness tradeoffs in machine learning with multiobjective meta-models.

William G. La Cava

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

Fair admission risk prediction with proportional multicalibration.

William G. La Cava, Elle Lett, Guangya Wan

Proceedings of the Conference on Health, Inference, and Learning, 2023

2022

Proportional Multicalibration.

William G. La Cava, Elle Lett, Guangya Wan

CoRR, 2022

PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods.

Joseph D. Romano, Trang T. Le, William G. La Cava, John T. Gregg, Daniel J. Goldberg, Praneel Chakraborty, Natasha L. Ray, Daniel S. Himmelstein, Weixuan Fu, Jason H. Moore

Bioinform., 2022

Population Diversity Leads to Short Running Times of Lexicase Selection.

Thomas Helmuth, Johannes Lengler, William G. La Cava

Proceedings of the Parallel Problem Solving from Nature - PPSN XVII, 2022

A comparative study of GP-based and state-of-the-art classifiers on a synthetic machine learning benchmark.

Patryk Orzechowski, Pawel Renc, William G. La Cava, Jason H. Moore, Arkadiusz Sitek, Jaroslaw Was, Joost B. Wagenaar

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming.

Nuno M. Rodrigues, João E. Batista, William G. La Cava, Leonardo Vanneschi, Sara Silva

Proceedings of the Genetic Programming - 25th European Conference, 2022

2021

Evaluating recommender systems for AI-driven biomedical informatics.

William G. La Cava, Heather Williams, Weixuan Fu, Steven Vitale, Durga Srivatsan, Jason H. Moore

Bioinform., 2021

Contemporary Symbolic Regression Methods and their Relative Performance.

William G. La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Lexicase Selection.

Thomas Helmuth, William G. La Cava

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

2020

Learning feature spaces for regression with genetic programming.

William G. La Cava, Jason H. Moore

Genet. Program. Evolvable Mach., 2020

Theory Manual for the Tuned Mass Damper Module in FAST v8.

William G. La Cava, Matthew A. Lackner

CoRR, 2020

Genetic programming approaches to learning fair classifiers.

William G. La Cava, Jason H. Moore

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

2019

Multidimensional genetic programming for multiclass classification.

William G. La Cava, Sara Silva, Kourosh Danai, Lee Spector, Leonardo Vanneschi, Jason H. Moore

Swarm Evol. Comput., 2019

A Probabilistic and Multi-Objective Analysis of Lexicase Selection and ε-Lexicase Selection.

William G. La Cava, Thomas Helmuth, Lee Spector, Jason H. Moore

Evol. Comput., 2019

Evaluating recommender systems for AI-driven data science.

William G. La Cava, Heather Williams, Weixuan Fu, Jason H. Moore

CoRR, 2019

Learning concise representations for regression by evolving networks of trees.

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

Proceedings of the 7th International Conference on Learning Representations, 2019

Semantic variation operators for multidimensional genetic programming.

William G. La Cava, Jason H. Moore

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Interpretation of machine learning predictions for patient outcomes in electronic health records.

William G. La Cava, Christopher R. Bauer, Jason H. Moore, Sarah A. Pendergrass

Proceedings of the AMIA 2019, 2019

2018

Relief-based feature selection: Introduction and review.

Ryan J. Urbanowicz, Melissa Meeker, William G. La Cava, Randal S. Olson, Jason H. Moore

J. Biomed. Informatics, 2018

Stochastic optimization approaches to learning concise representations.

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

CoRR, 2018

Data-driven advice for applying machine learning to bioinformatics problems.

Randal S. Olson, William G. La Cava, Zairah Mustahsan, Akshay Varik, Jason H. Moore

Proceedings of the Biocomputing 2018: Proceedings of the Pacific Symposium, 2018

Behavioral search drivers and the role of elitism in soft robotics.

William G. La Cava, Jason H. Moore

Proceedings of the 2018 Conference on Artificial Life, 2018

Where are we now?: a large benchmark study of recent symbolic regression methods.

Patryk Orzechowski, William G. La Cava, Jason H. Moore

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

A multidimensional genetic programming approach for identifying epsistatic gene interactions.

William G. La Cava, Sara Silva, Kourosh Danai, Lee Spector, Leonardo Vanneschi, Jason H. Moore

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

An analysis of ϵ-lexicase selection for large-scale many-objective optimization.

William G. La Cava, Jason H. Moore

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

2017

$ε$-Lexicase selection: a probabilistic and multi-objective analysis of lexicase selection in continuous domains.

William G. La Cava, Thomas Helmuth, Lee Spector, Jason H. Moore

CoRR, 2017

A System for Accessible Artificial Intelligence.

Randal S. Olson, Moshe Sipper, William G. La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, John H. Holmes, Jason H. Moore

CoRR, 2017

PMLB: a large benchmark suite for machine learning evaluation and comparison.

Randal S. Olson, William G. La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore

BioData Min., 2017

Relaxations of Lexicase Parent Selection.

Lee Spector, William G. La Cava, Saul Shanabrook, Thomas Helmuth, Edward R. Pantridge

Proceedings of the Genetic Programming Theory and Practice XV, 2017

A System for Accessible Artificial Intelligence.

Randal S. Olson, Moshe Sipper, William G. La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, Patryk Orzechowski, Ryan J. Urbanowicz, John H. Holmes, Jason H. Moore

Proceedings of the Genetic Programming Theory and Practice XV, 2017

Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods.

William G. La Cava, Jason H. Moore

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

Genetic Programming Representations for Multi-dimensional Feature Learning in Biomedical Classification.

William G. La Cava, Sara Silva, Leonardo Vanneschi, Lee Spector, Jason H. Moore

Proceedings of the Applications of Evolutionary Computation - 20th European Conference, 2017

A General Feature Engineering Wrapper for Machine Learning Using \epsilon -Lexicase Survival.

William G. La Cava, Jason H. Moore

Proceedings of the Genetic Programming - 20th European Conference, 2017

2016

Gradient-based adaptation of continuous dynamic model structures.

William G. La Cava, Kourosh Danai

Int. J. Syst. Sci., 2016

Inference of compact nonlinear dynamic models by epigenetic local search.

William G. La Cava, Kourosh Danai, Lee Spector

Eng. Appl. Artif. Intell., 2016

Epsilon-Lexicase Selection for Regression.

William G. La Cava, Lee Spector, Kourosh Danai

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20, 2016

2015

Genetic Programming with Epigenetic Local Search.

William G. La Cava, Thomas Helmuth, Lee Spector, Kourosh Danai

Proceedings of the Genetic and Evolutionary Computation Conference, 2015

2014

Analyzing a Decade of Human-Competitive ("HUMIE") Winners: What Can We Learn?

Karthik Kannappan, Lee Spector, Moshe Sipper, Thomas Helmuth, William G. La Cava, Jake Wisdom, Omri Bernstein

Proceedings of the Genetic Programming Theory and Practice XII, 2014

Inheritable Epigenetics in Genetic Programming.

William G. La Cava, Lee Spector

Proceedings of the Genetic Programming Theory and Practice XII, 2014

Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing.

William G. La Cava, Lee Spector, Kourosh Danai, Matthew Lackner

Proceedings of the Genetic and Evolutionary Computation Conference, 2014