Samuel C. Hoffman

According to our database1, Samuel C. Hoffman authored at least 24 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
GP-MoLFormer: A Foundation Model For Molecular Generation.
CoRR, 2024

2023
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions.
CoRR, 2023

Searching for Fairer Machine Learning Ensembles.
Proceedings of the International Conference on Automated Machine Learning, 2023

2022
Optimizing molecules using efficient queries from property evaluations.
Nat. Mach. Intell., 2022

Navigating Ensemble Configurations for Algorithmic Fairness.
CoRR, 2022

Causal Graphs Underlying Generative Models: Path to Learning with Limited Data.
CoRR, 2022

GT4SD: Generative Toolkit for Scientific Discovery.
CoRR, 2022

Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework.
CoRR, 2022

An Empirical Study of Modular Bias Mitigators and Ensembles.
CoRR, 2022

Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations.
Proceedings of the IEEE International Conference on Acoustics, 2022


2021
Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model.
CoRR, 2021


2020
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models.
J. Mach. Learn. Res., 2020

Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models.
CoRR, 2020

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Combinatorial Black-Box Optimization with Expert Advice.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020


2019
Think Your Artificial Intelligence Software Is Fair? Think Again.
IEEE Softw., 2019

Fairness GAN: Generating datasets with fairness properties using a generative adversarial network.
IBM J. Res. Dev., 2019

AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias.
IBM J. Res. Dev., 2019

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques.
CoRR, 2019

2018
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias.
CoRR, 2018

Fairness GAN.
CoRR, 2018


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