Publications

By date | By topic

Optimizing fairness tradeoffs in machine learning with multiobjective meta-models
La Cava, W. G. (2023)
Genetic and Evolutionary Computation Conference (GECCO)
dl.acm.org  |  arXiv  |  experiments  |  software
Fair admission risk prediction with proportional multicalibration
La Cava, W., Lett, E., and Wan, G. (2023)
Conference on Health, Inference, and Learning
Proceedings of Machine Learning Research
proceedings.mlr.press  |  arXiv  |  experiments  |  software  |  Best Paper Award
A flexible symbolic regression method for constructing interpretable clinical prediction models
La Cava, W., Lee, P.C., Ajmal, I., Ding, X., Cohen, J.B., Solanki, P., Moore, J.H., and Herman, D.S (2023)
npj Digital Medicine
www.nature.com  |  medrxiv  |  HUMIES Silver Award  
Translating intersectionality to fair machine learning in health sciences
Lett, E. and La Cava, W. G. (2023)
Nature Machine Intelligence
www.nature.com  |  preprint
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition
de Franca, F. O., Virgolin, M., Kommenda, M., Majumder, M. S., Cranmer, M., Espada, G., ... & La Cava, W. G. (2023)
Preprint
arXiv  |  project page
Population Diversity Leads to Short Running Times of Lexicase Selection
Helmuth, T., Lengler, J., and La Cava, W. (2022)
Parallel Problem Solving from Nature (PPSN)
link.springer.com  |  arXiv
A Comparative Study of GP-based and State-of-the-art Classifiers on a Synthetic Machine Learning Benchmark
Orzechowski, P., Renc, P., La Cava, W., Moore, J. H., Sitek, A., Wąs, J., and Wagenaar, J. (2022)
Genetic and Evolutionary Computation Conference (GECCO)
dl.acm.org  |  pdf
Algorithmic Fairness: Mitigating Bias in Healthcare AI
Lett, E. (2022)
MedScape
www.medscape.com  |  pdf
SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming
Rodrigues, N. M., Batista, J. E., La Cava, W., Vanneschi, L., and Silva, S. (2022)
European Conference on Genetic Programming (EuroGP)
Lecture Notes in Computer Science
link.springer.com
Informative Missingness: What can we learn from patterns in missing laboratory data in the electronic health record?
Tan, A. L., Getzen, E. J., Hutch, M. R., Strasser, Z. H., Gutiérrez-Sacristán, A., Le, T. T., ... and Holmes, J. H. (2022)
Journal of Biomedical Informatics
doi.org  |  medrxiv
PMLB v1. 0: an open-source dataset collection for benchmarking machine learning methods
Romano, J. D., Le, T. T., La Cava, W., Gregg, J. T., Goldberg, D. J., Chakraborty, P., ... and Moore, J. H. (2022)
Bioinformatics
academic.oup.com  |  arXiv  |  software
Contemporary Symbolic Regression Methods and their Relative Performance
La Cava, W., Orzechowski, P., Burlacu, B., França, F. O. de, Virgolin, M., Jin, Y., Kommenda, M., and Moore, J. H. (2021)
Neurips Track on Datasets and Benchmarks
datasets-benchmarks-proceedings.neurips.cc  |  arXiv  |  project page
Controller design by symbolic regression
Danai, K. and La Cava, W.G. (2021)
Mechanical Systems and Signal Processing
www.sciencedirect.com
Learning feature spaces for regression with genetic programming
La Cava, W. and Moore, J.H. (2020)
Genetic Programming and Evolvable Machines (GPEM)
link.springer.com  |  pdf  |  experiments  |  software
Evaluating recommender systems for AI-driven biomedical informatics
La Cava, W., Williams, H., Fu, W., Vitale, S., Srivatsan, D., Moore, J. H. (2020)
Bioinformatics
doi.org  |  experiments  |  software
Benchmarking in Optimization: Best Practice and Open Issues
Bartz-Beielstein, T., Doerr, C., Berg, D. van den, Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., La Cava, W., Lopez-Ibanez, M., Malan, K. M., Moore, J. H., Naujoks, B., Orzechowski, P., Volz, V., Wagner, M., and Weise, T. (2020)
Preprint
arXiv
Genetic programming approaches to learning fair classifiers
La Cava, W. and Moore, Jason H. (2020)
Genetic and Evolutionary Computation Conference (GECCO)
dl.acm.org  |  arXiv  |  experiments  |  software  |  Best Paper Award
Interpretation of machine learning predictions for patient outcomes in electronic health records
La Cava, W., Bauer, C. R., Moore, J. H., and Pendergrass, S. A. (2019)
AMIA Annual Symposium
www.ncbi.nlm.nih.gov  |  arXiv  |  experiments
Machine Learning to Predict Toxicity in Head and Neck Cancer Patients Treated with Definitive Chemoradiation
Wojcieszynski Jr, A. P., La Cava, W., Baumann, B. C., Lukens, J. N., Fotouhi Ghiam, A., Urbanowicz, R. J., … Metz, J. M. (2019)
International Journal of Radiation Oncology • Biology • Physics
www.redjournal.org  |  pdf
A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection
La Cava, W., Helmuth, T., Spector, L., and Moore, J. H. (2019)
Evolutionary Computation Journal
doi.org  |  arXiv  |  experiments  |  software
Semantic variation operators for multidimensional genetic programming
La Cava, W., and Moore, J. H. (2019)
Genetic and Evolutionary Computation Conference (GECCO)
doi.org  |  arXiv  |  experiments  |  software
Learning concise representations for regression by evolving networks of trees
La Cava, W., and Moore, J. H. (2019)
International Conference on Learning Representations (ICLR)
openreview.net  |  arXiv  |  experiments  |  software
Multidimensional genetic programming for multiclass classification
La Cava, W., Silva, S., Danai, K., Spector, L., Vanneschi, L., and Moore, J. H. (2019)
Swarm and Evolutionary Computation
doi.org  |  pdf  |  software
Relief-based feature selection: Introduction and review
Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., and Moore, J. H. (2018)
Journal of Biomedical Informatics
doi.org  |  arXiv
Relaxations of lexicase parent selection
Spector, L., La Cava, W., Shanabrook, S., Helmuth, T., & Pantridge, E. (2018)
Genetic Programming Theory and Practice XV
link.springer.com  |  pdf
An Analysis of epsilon-lexicase Selection for Large-scale Many-objective Optimization
La Cava, W., and Moore, J. H. (2018)
Genetic and Evolutionary Computation Conference (GECCO)
doi.org  |  preprint  |  experiments
Where are we now? A large benchmark study of recent symbolic regression methods
Orzechowski, P., La Cava, W., and Moore, J. H. (2018)
Genetic and Evolutionary Computation Conference (GECCO)
doi.org  |  arXiv  |  software
Behavioral search drivers and the role of elitism in soft robotics
La Cava, W., and Moore, J. H. (2018)
Artificial Life
doi.org  |  experiments
A System for Accessible Artificial Intelligence
Olson, R. S., Sipper, M., La Cava, W., Tartarone, S., Vitale, S., Fu, W., Holmes, J. H., & Moore, J. H. (2017)
Genetic Programming Theory and Practice XIV.
link.springer.com  |  arXiv
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison
Olson, R. S., La Cava, W., Orzechowski, P., Urbanowicz, R. J., and Moore, J. H. (2017)
BioData Mining
biodatamining.biomedcentral.com  |  arXiv  |  software
Data-driven Advice for Applying Machine Learning to Bioinformatics Problems
Olson*, R. S., La Cava*, William, Mustahsan, Z., Varik, A., and Moore, J. H. (2017)
Pacific Symposium on Biocomputing (PSB)
www.worldscientific.com  |  arXiv  |  experiments
Restructuring Controllers to Accommodate Plant Nonlinearities
La Cava, W., Sahare, K., and Danai, K. (2017)
Journal of Dynamic Systems, Measurement, and Control
doi.org  |  pdf
Ensemble representation learning: An analysis of fitness and survival for wrapper-based genetic programming methods
La Cava, W., and Moore, J. H. (2017)
Genetic and Evolutionary Computation Conference (GECCO)
dl.acm.org  |  arXiv  |  software
Genetic Programming Representations for Multi-dimensional Feature Learning in Biomedical Classification
La Cava, W., Silva, S., Vanneschi, L., Spector, L., and Moore, J. (2017)
Evo*: European Conference on the Applications of Evolutionary Computation
Lecture Notes in Computer Science
doi.org  |  pdf
A general feature engineering wrapper for machine learning using ϵ-lexicase survival
La Cava, W., and Moore, J. H. (2017)
European Conference on Genetic Programming (EuroGP)
doi.org  |  pdf  |  software
Inference of compact nonlinear dynamic models by epigenetic local search
La Cava, W., Danai, K., and Spector, L. (2016)
Engineering Applications of Artificial Intelligence
doi.org  |  pdf
Epsilon-Lexicase Selection for Regression
La Cava, W., Spector, L., and Danai, K. (2016)
Genetic and Evolutionary Computation Conference (GECCO)
doi.org  |  arXiv