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02/17/2023
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions
Ensuring trustworthiness in machine learning (ML) models is a multi-dime...
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10/11/2022
Navigating Ensemble Configurations for Algorithmic Fairness
Bias mitigators can improve algorithmic fairness in machine learning mod...
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07/14/2022
Causal Graphs Underlying Generative Models: Path to Learning with Limited Data
Training generative models that capture rich semantics of the data and i...
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07/08/2022
GT4SD: Generative Toolkit for Scientific Discovery
With the growing availability of data within various scientific domains,...
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04/19/2022
Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework
The COVID-19 pandemic has highlighted the urgency for developing more ef...
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02/01/2022
An Empirical Study of Modular Bias Mitigators and Ensembles
There are several bias mitigators that can reduce algorithmic bias in ma...
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12/02/2021
Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model
Photo-acid generators (PAGs) are compounds that release acids (H^+ ions)...
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09/24/2021
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increa...
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04/02/2020
Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
The recent COVID-19 pandemic has highlighted the need for rapid therapeu...
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09/06/2019
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
As artificial intelligence and machine learning algorithms make further ...
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10/03/2018
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Fairness is an increasingly important concern as machine learning models...
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05/24/2018
