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ORCIDWilliam G. La Cava
William George La Cava
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- affiliation: Boston Children's Hospital, MA, USA
- affiliation: University of Pennsylvania, Institute for Biomedical Informatics, Philadelphia, PA, USA
- affiliation: University of Massachusetts Amherst, MA, USA
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2020 – today
- 2024
[j13]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): 91 (2024)- [c30]Guilherme Seidyo Imai Aldeia, Fabrício Olivetti de França, William G. La Cava:
Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing. GECCO 2024 - [c29]Guilherme Seidyo Imai Aldeia, Fabrício Olivetti de França, William G. La Cava:
Minimum variance threshold for epsilon-lexicase selection. GECCO 2024 - [i24]Guilherme Seidyo Imai Aldeia, Fabrício Olivetti de França, William G. La Cava:
Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing. CoRR abs/2404.05898 (2024) - [i23]Guilherme Seidyo Imai Aldeia, Fabrício Olivetti de França, William G. La Cava:
Minimum variance threshold for epsilon-lexicase selection. CoRR abs/2404.05909 (2024) - [i22]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 abs/2405.05506 (2024) - [i21]Platon Lukyanenko, Joshua Mayourian, Mingxuan Liu, John K. Triedman, Sunil J. Ghelani, William G. La Cava:
Benchmarking mortality risk prediction from electrocardiograms. CoRR abs/2406.17002 (2024) - 2023
- [j12]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:
Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record? J. Biomed. Informatics 139: 104306 (2023) - [j11]Elle Lett, William G. La Cava:
Translating intersectionality to fair machine learning in health sciences. Nat. Mac. Intell. 5(5): 476-479 (2023) - [j10]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) - [c28]William G. La Cava, Elle Lett, Guangya Wan:
Fair admission risk prediction with proportional multicalibration. CHIL 2023: 350-378 - [c27]William G. La Cava:
Optimizing fairness tradeoffs in machine learning with multiobjective meta-models. GECCO 2023: 511-519 - [c26]William George La Cava, Thomas Helmuth:
Lexicase Selection. GECCO Companion 2023: 976-989 - [i20]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:
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR abs/2304.01117 (2023) - [i19]William G. La Cava:
Optimizing fairness tradeoffs in machine learning with multiobjective meta-models. CoRR abs/2304.12190 (2023) - 2022
- [j9]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): 878-880 (2022) - [c25]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: 68-84 - [c24]Patryk Orzechowski, Pawel Renc, William G. La Cava, Jason H. Moore, Arkadiusz Sitek, Jaroslaw Was, Joost B. Wagenaar:
A comparative study of GP-based and state-of-the-art classifiers on a synthetic machine learning benchmark. GECCO Companion 2022: 276-279 - [c23]Thomas Helmuth, William G. La Cava:
Lexicase selection. GECCO Companion 2022: 1385-1397 - [c22]Thomas Helmuth, Johannes Lengler, William G. La Cava:
Population Diversity Leads to Short Running Times of Lexicase Selection. PPSN (2) 2022: 485-498 - [i18]Thomas Helmuth, Johannes Lengler, William G. La Cava:
Population Diversity Leads to Short Running Times of Lexicase Selection. CoRR abs/2204.06461 (2022) - [i17]William G. La Cava, Elle Lett, Guangya Wan:
Proportional Multicalibration. CoRR abs/2209.14613 (2022) - 2021
- [j8]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): 250-256 (2021) - [c21]Thomas Helmuth, William G. La Cava:
Lexicase Selection. GECCO Companion 2021: 839-855 - [c20]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 - [i16]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 abs/2107.14351 (2021) - 2020
- [j7]William G. La Cava, Jason H. Moore:
Learning feature spaces for regression with genetic programming. Genet. Program. Evolvable Mach. 21(3): 433-467 (2020) - [c19]William G. La Cava, Jason H. Moore:
Genetic programming approaches to learning fair classifiers. GECCO 2020: 967-975 - [i15]William G. La Cava, Jason H. Moore:
Genetic programming approaches to learning fair classifiers. CoRR abs/2004.13282 (2020) - [i14]William G. La Cava, Matthew A. Lackner:
Theory Manual for the Tuned Mass Damper Module in FAST v8. CoRR abs/2008.02650 (2020) - [i13]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 abs/2012.00058 (2020)
2010 – 2019
- 2019
- [j6]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): 377-402 (2019) - [j5]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: 260-272 (2019) - [c18]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 - [c17]William G. La Cava, Jason H. Moore:
Semantic variation operators for multidimensional genetic programming. GECCO 2019: 1056-1064 - [c16]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 - [i12]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 abs/1903.12074 (2019) - [i11]William G. La Cava, Jason H. Moore:
Semantic variation operators for multidimensional genetic programming. CoRR abs/1904.08577 (2019) - [i10]William G. La Cava, Heather Williams, Weixuan Fu, Jason H. Moore:
Evaluating recommender systems for AI-driven data science. CoRR abs/1905.09205 (2019) - [i9]William G. La Cava, Lee Spector, Kourosh Danai:
Epsilon-Lexicase Selection for Regression. CoRR abs/1905.13266 (2019) - 2018
- [j4]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: 189-203 (2018) - [c15]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: 23-24 - [c14]William G. La Cava, Jason H. Moore:
An analysis of ϵ-lexicase selection for large-scale many-objective optimization. GECCO (Companion) 2018: 185-186 - [c13]Patryk Orzechowski, William G. La Cava, Jason H. Moore:
Where are we now?: a large benchmark study of recent symbolic regression methods. GECCO 2018: 1183-1190 - [c12]William G. La Cava, Jason H. Moore:
Behavioral search drivers and the role of elitism in soft robotics. ALIFE 2018: 206-213 - [c11]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: 192-203 - [i8]Patryk Orzechowski, William G. La Cava, Jason H. Moore:
Where are we now? A large benchmark study of recent symbolic regression methods. CoRR abs/1804.09331 (2018) - [i7]William G. La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore:
Stochastic optimization approaches to learning concise representations. CoRR abs/1807.00981 (2018) - 2017
- [j3]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): 36:1-36:13 (2017) - [c10]William G. La Cava, Jason H. Moore:
A General Feature Engineering Wrapper for Machine Learning Using \epsilon -Lexicase Survival. EuroGP 2017: 80-95 - [c9]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: 158-173 - [c8]William G. La Cava, Jason H. Moore:
Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods. GECCO 2017: 961-968 - [c7]Lee Spector, William G. La Cava, Saul Shanabrook, Thomas Helmuth, Edward R. Pantridge:
Relaxations of Lexicase Parent Selection. GPTP 2017: 105-120 - [c6]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: 121-134 - [i6]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 abs/1703.00512 (2017) - [i5]William G. La Cava, Jason H. Moore:
Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods. CoRR abs/1703.06934 (2017) - [i4]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 abs/1705.00594 (2017) - [i3]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 abs/1708.05070 (2017) - [i2]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 abs/1709.05394 (2017) - [i1]Ryan J. Urbanowicz, Melissa Meeker, William G. La Cava, Randal S. Olson, Jason H. Moore:
Relief-Based Feature Selection: Introduction and Review. CoRR abs/1711.08421 (2017) - 2016
- [j2]William G. La Cava, Kourosh Danai, Lee Spector:
Inference of compact nonlinear dynamic models by epigenetic local search. Eng. Appl. Artif. Intell. 55: 292-306 (2016) - [j1]William G. La Cava, Kourosh Danai:
Gradient-based adaptation of continuous dynamic model structures. Int. J. Syst. Sci. 47(1): 249-263 (2016) - [c5]William G. La Cava, Lee Spector, Kourosh Danai:
Epsilon-Lexicase Selection for Regression. GECCO 2016: 741-748 - 2015
- [c4]William G. La Cava, Thomas Helmuth, Lee Spector, Kourosh Danai:
Genetic Programming with Epigenetic Local Search. GECCO 2015: 1055-1062 - 2014
- [c3]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: 141-142 - [c2]William G. La Cava, Lee Spector:
Inheritable Epigenetics in Genetic Programming. GPTP 2014: 37-51 - [c1]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: 149-166
Coauthor Index
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