William G. La Cava

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 63

Top Topics

Benchmark Suite

Genetic Programming

Regression Methods

Top Venues

GECCO Companion

Publications

Nuno M. Rodrigues, João E. Batista, William G. La Cava, Leonardo Vanneschi, Sara Silva
Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. SN Comput. Sci. 5 (1) (2024)
Guilherme Seidyo Imai Aldeia, Fabrício Olivetti de França, William G. La Cava
Minimum variance threshold for epsilon-lexicase selection. GECCO (2024)
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
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias. CoRR (2024)
Guilherme Seidyo Imai Aldeia, Fabrício Olivetti de França, William G. La Cava
Minimum variance threshold for epsilon-lexicase selection. CoRR (2024)
Platon Lukyanenko, Joshua Mayourian, Mingxuan Liu, John K. Triedman, Sunil J. Ghelani, William G. La Cava
Benchmarking mortality risk prediction from electrocardiograms. CoRR (2024)
Guilherme Seidyo Imai Aldeia, Fabrício Olivetti de França, William G. La Cava
Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing. CoRR (2024)
Guilherme Seidyo Imai Aldeia, Fabrício Olivetti de França, William G. La Cava
Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing. GECCO (2024)
William G. La Cava
Optimizing fairness tradeoffs in machine learning with multiobjective meta-models. GECCO (2023)
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, Priam 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
Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record? J. Biomed. Informatics 139 (2023)
William G. La Cava, Paul C. Lee, Imran Ajmal, Xiruo Ding, Priyanka Solanki, Jordana B. Cohen, Jason H. Moore, Daniel S. Herman
A flexible symbolic regression method for constructing interpretable clinical prediction models. npj Digit. Medicine 6 (2023)
William G. La Cava
Optimizing fairness tradeoffs in machine learning with multiobjective meta-models. CoRR (2023)
Elle Lett, William G. La Cava
Translating intersectionality to fair machine learning in health sciences. Nat. Mac. Intell. 5 (5) (2023)
Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, M. S. Majumder, Miles D. Cranmer, Guilherme Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, B. Petersen, Ruben Glatt, T. Nathan Mundhenk, C. S. Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, G. Dick, A. Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR (2023)
William G. La Cava, Elle Lett, Guangya Wan
Fair admission risk prediction with proportional multicalibration. CHIL (2023)
Thomas Helmuth, Johannes Lengler, William G. La Cava
Population Diversity Leads to Short Running Times of Lexicase Selection. PPSN (2) (2022)
William G. La Cava, Elle Lett, Guangya Wan
Proportional Multicalibration. CoRR (2022)
Thomas Helmuth, Johannes Lengler, William G. La Cava
Population Diversity Leads to Short Running Times of Lexicase Selection. CoRR (2022)
Patryk Orzechowski, Pawel Renc, William G. La Cava, Jason H. Moore, Arkadiusz Sitek, Jaroslaw Was, Joost Wagenaar
A comparative study of GP-based and state-of-the-art classifiers on a synthetic machine learning benchmark. GECCO Companion (2022)
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
PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods. Bioinform. 38 (3) (2022)
Thomas Helmuth, William G. La Cava
Lexicase selection. GECCO Companion (2022)
Nuno M. Rodrigues, João E. Batista, William G. La Cava, Leonardo Vanneschi, Sara Silva
SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. EuroGP (2022)
Thomas Helmuth, William G. La Cava
Lexicase Selection. GECCO Companion (2021)
William G. La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore
Contemporary Symbolic Regression Methods and their Relative Performance. NeurIPS Datasets and Benchmarks (2021)
William G. La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore
Contemporary Symbolic Regression Methods and their Relative Performance. CoRR (2021)
William G. La Cava, Heather Williams, Weixuan Fu, Steven Vitale, Durga Srivatsan, Jason H. Moore
Evaluating recommender systems for AI-driven biomedical informatics. Bioinform. 37 (2) (2021)
William G. La Cava, Jason H. Moore
Learning feature spaces for regression with genetic programming. Genet. Program. Evolvable Mach. 21 (3) (2020)
Trang T. Le, William G. La Cava, Joseph D. Romano, John T. Gregg, Daniel J. Goldberg, Praneel Chakraborty, Natasha L. Ray, Daniel S. Himmelstein, Weixuan Fu, Jason H. Moore
PMLB v1.0: an open source dataset collection for benchmarking machine learning methods. CoRR (2020)
William G. La Cava, Matthew A. Lackner
Theory Manual for the Tuned Mass Damper Module in FAST v8. CoRR (2020)
William G. La Cava, Jason H. Moore
Genetic programming approaches to learning fair classifiers. CoRR (2020)
William G. La Cava, Jason H. Moore
Genetic programming approaches to learning fair classifiers. GECCO (2020)
William G. La Cava, Lee Spector, Kourosh Danai
Epsilon-Lexicase Selection for Regression. CoRR (2019)
William G. La Cava, Thomas Helmuth, Lee Spector, Jason H. Moore
A Probabilistic and Multi-Objective Analysis of Lexicase Selection and ε-Lexicase Selection. Evol. Comput. 27 (3) (2019)
William G. La Cava, Christopher R. Bauer, Jason H. Moore, Sarah A. Pendergrass
Interpretation of machine learning predictions for patient outcomes in electronic health records. AMIA (2019)
William G. La Cava, Sara Silva, Kourosh Danai, Lee Spector, Leonardo Vanneschi, Jason H. Moore
Multidimensional genetic programming for multiclass classification. Swarm Evol. Comput. 44 (2019)
William G. La Cava, Jason H. Moore
Semantic variation operators for multidimensional genetic programming. GECCO (2019)
William G. La Cava, Christopher R. Bauer, Jason H. Moore, Sarah A. Pendergrass
Interpretation of machine learning predictions for patient outcomes in electronic health records. CoRR (2019)
William G. La Cava, Heather Williams, Weixuan Fu, Jason H. Moore
Evaluating recommender systems for AI-driven data science. CoRR (2019)
William G. La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore
Learning concise representations for regression by evolving networks of trees. ICLR (Poster) (2019)
William G. La Cava, Jason H. Moore
Semantic variation operators for multidimensional genetic programming. CoRR (2019)
William G. La Cava, Jason H. Moore
An analysis of ϵ-lexicase selection for large-scale many-objective optimization. GECCO (Companion) (2018)
William G. La Cava, Sara Silva, Kourosh Danai, Lee Spector, Leonardo Vanneschi, Jason H. Moore
A multidimensional genetic programming approach for identifying epsistatic gene interactions. GECCO (Companion) (2018)
William G. La Cava, Jason H. Moore
Behavioral search drivers and the role of elitism in soft robotics. ALIFE (2018)
Patryk Orzechowski, William G. La Cava, Jason H. Moore
Where are we now?: a large benchmark study of recent symbolic regression methods. GECCO (2018)
Randal S. Olson, William G. La Cava, Zairah Mustahsan, Akshay Varik, Jason H. Moore
Data-driven advice for applying machine learning to bioinformatics problems. PSB (2018)
Patryk Orzechowski, William G. La Cava, Jason H. Moore
Where are we now? A large benchmark study of recent symbolic regression methods. CoRR (2018)
William G. La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore
Stochastic optimization approaches to learning concise representations. CoRR (2018)
Ryan J. Urbanowicz, Melissa Meeker, William G. La Cava, Randal S. Olson, Jason H. Moore
Relief-based feature selection: Introduction and review. J. Biomed. Informatics 85 (2018)
Randal S. Olson, Moshe Sipper, William G. La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, John H. Holmes, Jason H. Moore
A System for Accessible Artificial Intelligence. CoRR (2017)
William G. La Cava, Jason H. Moore
Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods. GECCO (2017)
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
A System for Accessible Artificial Intelligence. GPTP (2017)
Lee Spector, William G. La Cava, Saul Shanabrook, Thomas Helmuth, Edward R. Pantridge
Relaxations of Lexicase Parent Selection. GPTP (2017)
William G. La Cava, Sara Silva, Leonardo Vanneschi, Lee Spector, Jason H. Moore
Genetic Programming Representations for Multi-dimensional Feature Learning in Biomedical Classification. EvoApplications (1) (2017)
Randal S. Olson, William G. La Cava, Zairah Mustahsan, Akshay Varik, Jason H. Moore
Data-driven Advice for Applying Machine Learning to Bioinformatics Problems. CoRR (2017)
William G. La Cava, Thomas Helmuth, Lee Spector, Jason H. Moore
$ε$-Lexicase selection: a probabilistic and multi-objective analysis of lexicase selection in continuous domains. CoRR (2017)
Randal S. Olson, William G. La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison. CoRR (2017)
Ryan J. Urbanowicz, Melissa Meeker, William G. La Cava, Randal S. Olson, Jason H. Moore
Relief-Based Feature Selection: Introduction and Review. CoRR (2017)
Randal S. Olson, William G. La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore
PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Min. 10 (1) (2017)
William G. La Cava, Jason H. Moore
A General Feature Engineering Wrapper for Machine Learning Using \epsilon -Lexicase Survival. EuroGP (2017)
William G. La Cava, Jason H. Moore
Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods. CoRR (2017)
William G. La Cava, Kourosh Danai
Gradient-based adaptation of continuous dynamic model structures. Int. J. Syst. Sci. 47 (1) (2016)
William G. La Cava, Lee Spector, Kourosh Danai
Epsilon-Lexicase Selection for Regression. GECCO (2016)
William G. La Cava, Kourosh Danai, Lee Spector
Inference of compact nonlinear dynamic models by epigenetic local search. Eng. Appl. Artif. Intell. 55 (2016)
William G. La Cava, Thomas Helmuth, Lee Spector, Kourosh Danai
Genetic Programming with Epigenetic Local Search. GECCO (2015)
Karthik Kannappan, Lee Spector, Moshe Sipper, Thomas Helmuth, William G. La Cava, Jake Wisdom, Omri Bernstein
Analyzing a Decade of Human-Competitive ("HUMIE") Winners: What Can We Learn? GPTP (2014)
William G. La Cava, Lee Spector
Inheritable Epigenetics in Genetic Programming. GPTP (2014)
William G. La Cava, Lee Spector, Kourosh Danai, Matthew Lackner
Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing. GECCO (Companion) (2014)