Publications | Overview
La Cava, W., Orzechowski, P., Burlacu, B., França, F. O. de, Virgolin, M., Jin, Y., Kommenda, M., & Moore, J. H. (2021). Contemporary Symbolic Regression Methods and their Relative Performance. Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (Accepted). arXiv, project repository
La Cava, W., Lee, P.C., Ajmal, I., Ding, X., Cohen, J.B., Solanki, P., Moore, J.H., and Herman, D.S (2021). Application of concise machine learning to construct accurate and interpretable EHR computable phenotypes. In Review. medRxiv
La Cava, W. & Moore, Jason H. (2020). Genetic programming approaches to learning fair classifiers. GECCO 2020. Best Paper Award. ACM, arXiv, software, experiments
La Cava, W., Williams, H., Fu, W., Vitale, S., Srivatsan, D., Moore, J. H. (2020). Evaluating recommender systems for AI-driven biomedical informatics. Bioinformatics. Oxford Press (open access), software, experiments
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., & Weise, T. (2020). Benchmarking in Optimization: Best Practice and Open Issues. arXiv
Le, T. T., La Cava, W., Romano, J. D., Gregg, J. T., Goldberg, D. J., Chakraborty, P., Ray, N. L., Himmelstein, D., Fu, W., & Moore, J. H. (2020). PMLB v1.0: An open source dataset collection for benchmarking machine learning methods. arXiv, software
La Cava, W. & Moore, J.H. (2020). Learning feature spaces for regression with genetic programming. Genetic Programming and Evolvable Machines (GPEM). Springer, PDF, software, experiments
La Cava, W., Bauer, C. R., Moore, J. H., & Pendergrass, S. A. (2019). Interpretation of machine learning predictions for patient outcomes in electronic health records. AMIA 2019 Annual Symposium. arXiv, experiments
La Cava, W., & Moore, J. H. (2019). Semantic variation operators for multidimensional genetic programming. GECCO 2019. ACM, arXiv, software, experiments
La Cava, W., & Moore, J. H. (2019). Learning concise representations for regression by evolving networks of trees. ICLR 2019. arXiv, software, experiments
Wojcieszynski Jr, A. P., La Cava, W., Baumann, B. C., Lukens, J. N., Fotouhi Ghiam, A., Urbanowicz, R. J., Metz, J. M. (2019). Machine Learning to Predict Toxicity in Head and Neck Cancer Patients Treated with Definitive Chemoradiation. International Journal of Radiation Oncology • Biology • Physics. ASTRO, PDF
La Cava, W., Silva, S., Danai, K., Spector, L., Vanneschi, L., & Moore, J. H. (2019). Multidimensional genetic programming for multiclass classification. Swarm and Evolutionary Computation. ScienceDirect, PDF, software
La Cava, W., Helmuth, T., Spector, L., & Moore, J. H. (2018). A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection. Evolutionary Computation, 1–28. MIT Press, arXiv, experiments, software
La Cava, W., & Moore, J. H. (2018). An Analysis of epsilon-lexicase Selection for Large-scale Many-objective Optimization. GECCO 2018 Companion. ACM, experiments
La Cava, W., & Moore, J. H. (2018). Behavioral search drivers and the role of elitism in soft robotics. Artificial Life, 206–213. MIT Press (open access), project repository
Orzechowski, P., La Cava, W., & Moore, J. H. (2018). Where are we now? A large benchmark study of recent symbolic regression methods. GECCO 2018. ACM, arXiv, project repository
Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018). Relief-based feature selection: Introduction and review. Journal of Biomedical Informatics, 85, 189 – 203. ScienceDirect, arXiv
La Cava, W., & Moore, J. H. (2017). A general feature engineering wrapper for machine learning using ϵ-lexicase survival. European Conference on Genetic Programming, 80–95. Springer, PDF, software
La Cava, W., & Moore, J. H. (2017). Ensemble representation learning: An analysis of fitness and survival for wrapper-based genetic programming methods. GECCO 2017. ACM, arXiv, software
La Cava, W., Sahare, K., & Danai, K. (2017). Restructuring Controllers to Accommodate Plant Nonlinearities. Journal of Dynamic Systems, Measurement, and Control, 139(8), 081004–081004–10. https://doi.org/10.1115/1.4035870, PDF
La Cava, W., Silva, S., Vanneschi, L., Spector, L., & Moore, J. (2017). Genetic Programming Representations for Multi-dimensional Feature Learning in Biomedical Classification. EvoStar Applications of Evolutionary Computation, 10199, 158–173. Springer, PDF
Olson, R. S., La Cava, W., Orzechowski, P., Urbanowicz, R. J., & Moore, J. H. (2017). PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison. BioData Mining. BMC (open access), arXiv, software
Olson, R. S., La Cava, William, Mustahsan, Z., Varik, A., & Moore, J. H. (2017). Data-driven Advice for Applying Machine Learning to Bioinformatics Problems. Pacific Symposium on Biocomputing (PSB). arXiv, experiments
La Cava, W., Danai, K., & Spector, L. (2016). Inference of compact nonlinear dynamic models by epigenetic local search. Engineering Applications of Artificial Intelligence, 55, 292–306. https://doi.org/10.1016/j.engappai.2016.07.004, PDF
La Cava, W., Spector, L., & Danai, K. (2016). Epsilon-Lexicase Selection for Regression. GECCO 2016. ACM, arXiv