Search Results for author: William La Cava

Found 17 papers, 15 papers with code

Fair admission risk prediction with proportional multicalibration

1 code implementation29 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.

Fairness

Population Diversity Leads to Short Running Times of Lexicase Selection

no code implementations13 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).

Program Synthesis

Genetic programming approaches to learning fair classifiers

2 code implementations28 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.

Decision Making Fairness

Epsilon-Lexicase Selection for Regression

1 code implementation30 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.

regression Symbolic Regression

Evaluating recommender systems for AI-driven biomedical informatics

2 code implementations22 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.

BIG-bench Machine Learning Collaborative Filtering +2

Semantic variation operators for multidimensional genetic programming

1 code implementation18 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.

Interpretation of machine learning predictions for patient outcomes in electronic health records

1 code implementation14 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.

BIG-bench Machine Learning Feature Importance

Where are we now? A large benchmark study of recent symbolic regression methods

1 code implementation25 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.

Benchmarking BIG-bench Machine Learning +2

A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection

1 code implementation15 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.

Program Synthesis regression +1

A System for Accessible Artificial Intelligence

2 code implementations1 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.

BIG-bench Machine Learning

Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods

1 code implementation20 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.

Feature Engineering General Classification +1

PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

1 code implementation1 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.

Benchmarking BIG-bench Machine Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.