1 code implementation • 29 Sep 2022 • William La Cava, Elle Lett, Guangya Wan
We observe that proportional multicalibration is a promising criteria for controlling simultaneous measures of calibration fairness of a model over intersectional groups with virtually no cost in terms of classification performance.
no code implementations • 13 Apr 2022 • Thomas Helmuth, Johannes Lengler, William La Cava
In this paper we investigate why the running time of lexicase parent selection is empirically much lower than its worst-case bound of O(N*C).
4 code implementations • 29 Jul 2021 • William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore
We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.
3 code implementations • 30 Nov 2020 • Joseph D. Romano, Trang T. Le, William La Cava, John T. Gregg, Daniel J. Goldberg, Natasha L. Ray, Praneel Chakraborty, Daniel Himmelstein, Weixuan Fu, Jason H. Moore
Motivation: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets.
no code implementations • 7 Jul 2020 • Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, William La Cava, Manuel Lopez-Ibanez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, Thomas Weise
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world.
2 code implementations • 28 Apr 2020 • William La Cava, Jason H. Moore
In this paper, current approaches to fairness are discussed and used to motivate algorithmic proposals that incorporate fairness into genetic programming for classification.
1 code implementation • 30 May 2019 • William La Cava, Lee Spector, Kourosh Danai
We run a series of experiments on real-world and synthetic problems with several treatments of epsilon and quantify how epsilon affects parent selection and model performance.
2 code implementations • 22 May 2019 • William La Cava, Heather Williams, Weixuan Fu, Steve Vitale, Durga Srivatsan, Jason H. Moore
We have two goals in mind: first, to make it easy to construct sophisticated models of biomedical processes; and second, to provide a fully automated AI agent that can choose and conduct promising experiments for the user, based on the user's experiments as well as prior knowledge.
1 code implementation • 18 Apr 2019 • William La Cava, Jason H. Moore
Multidimensional genetic programming represents candidate solutions as sets of programs, and thereby provides an interesting framework for exploiting building block identification.
1 code implementation • 14 Mar 2019 • William La Cava, Christopher Bauer, Jason H. Moore, Sarah A Pendergrass
Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions.
3 code implementations • ICLR 2019 • William La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore
We propose and study a method for learning interpretable representations for the task of regression.
1 code implementation • 25 Apr 2018 • Patryk Orzechowski, William La Cava, Jason H. Moore
In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches.
1 code implementation • 15 Sep 2017 • William La Cava, Thomas Helmuth, Lee Spector, Jason H. Moore
Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection.
2 code implementations • 8 Aug 2017 • Randal S. Olson, William La Cava, Zairah Mustahsan, Akshay Varik, Jason H. Moore
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms.
2 code implementations • 1 May 2017 • Randal S. Olson, Moshe Sipper, William La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, Patryk Orzechowski, Ryan J. Urbanowicz, John H. Holmes, Jason H. Moore
While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them.
1 code implementation • 20 Mar 2017 • William La Cava, Jason H. Moore
Recently we proposed a general, ensemble-based feature engineering wrapper (FEW) that was paired with a number of machine learning methods to solve regression problems.
1 code implementation • 1 Mar 2017 • Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore
The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study.