Publications

20 results for Samuel Hoffman

One Explanation Does Not Fit All: A Toolkit And Taxonomy Of AI Explainability Techniques
- - Vijay Arya
  - Rachel K. Bellamy
  - et al.
- 2021
- INFORMS 2021
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
- - Vijil Chenthamarakshan
  - Payel Das
  - et al.
- 2020
- NeurIPS 2020
Combinatorial Black-Box Optimization with Expert Advice
- - Hamid Dadkhahi
  - Karthikeyan Shanmugam
  - et al.
- 2020
- KDD 2020
Chi-square Information for Invariant Learning
- - Prasanna Sattigeri
  - Soumya Ghosh
  - et al.
- 2020
- ICML 2020
Ai explainability 360: An extensible toolkit for understanding data and machine learning models
- - Vijay Arya
  - Rachel Bellamy
  - et al.
- 2020
- JMLR
Nano-Material Configuration Design with Deep Surrogate Langevin Dynamics
- - Thanh V. Nguyen
  - Youssef Mroueh
  - et al.
- 2020
- ICLR 2020
AI Explainability 360 Toolkit
- - Vijay Arya
  - Rachel Bellamy
  - et al.
- 2021
- CODS-COMAD 2021
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
- - Rachel Bellamy
  - Aleksandra Mojsilovic
  - et al.
- 2019
- IBM J. Res. Dev
Fairness GAN: Generating datasets with fairness properties using a generative adversarial network
- - Prasanna Sattigeri
  - Samuel C. Hoffman
  - et al.
- 2019
- IBM J. Res. Dev
Think Your Artificial Intelligence Software Is Fair? Think Again
- - Rachel Bellamy
  - Kuntal Dey
  - et al.
- 2019
- IEEE Software