Fairness GAN. - Prasanna Sattigeri; Samuel C. Hoffman; Vijil Chenthamarakshan; Kush R. Varshney (CoRR, 2018)

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias. - Rachel K. E. Bellamy; Kuntal Dey; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Kalapriya Kannan; Pranay Lohia; Jacquelyn Martino; Sameep Mehta; Aleksandra Mojsilovic; Seema Nagar; Karthikeyan Natesan Ramamurthy; et al. (CoRR, 2018)

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (CoRR, 2019)

AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. - Rachel K. E. Bellamy; Kuntal Dey; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Kalapriya Kannan; Pranay Lohia; Jacquelyn Martino; Sameep Mehta; Aleksandra Mojsilovic; Seema Nagar; Karthikeyan Natesan Ramamurthy; et al. (IBM J. Res. Dev., 2019)

Fairness GAN: Generating datasets with fairness properties using a generative adversarial network. - Prasanna Sattigeri; Samuel C. Hoffman; Vijil Chenthamarakshan; Kush R. Varshney (IBM J. Res. Dev., 2019)

Think Your Artificial Intelligence Software Is Fair? Think Again. - Rachel K. E. Bellamy; Kuntal Dey; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Kalapriya Kannan; Pranay Lohia; Sameep Mehta; Aleksandra Mojsilovic; Seema Nagar; Karthikeyan Natesan Ramamurthy; John T. Richards; et al. (IEEE Softw., 2019)

AI explainability 360: hands-on tutorial. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (FAT*, 2020)

Combinatorial Black-Box Optimization with Expert Advice. - Hamid Dadkhahi; Karthikeyan Shanmugam; Jesus Rios; Payel Das; Samuel C. Hoffman; Troy David Loeffler; Subramanian Sankaranarayanan (KDD, 2020)

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models. - Vijil Chenthamarakshan; Payel Das; Samuel C. Hoffman; Hendrik Strobelt; Inkit Padhi; Kar Wai Lim; Benjamin Hoover; Matteo Manica; Jannis Born; Teodoro Laino; Aleksandra Mojsilovic (NeurIPS, 2020)

Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models. - Vijil Chenthamarakshan; Payel Das; Inkit Padhi; Hendrik Strobelt; Kar Wai Lim; Benjamin Hoover; Samuel C. Hoffman; Aleksandra Mojsilovic (CoRR, 2020)

AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (J. Mach. Learn. Res., 2020)

AI Explainability 360 Toolkit. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (COMAD/CODS, 2021)

Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model. - Samuel C. Hoffman; Vijil Chenthamarakshan; Dmitry Yu. Zubarev; Daniel P. Sanders 002; Payel Das (CoRR, 2021)

AI Explainability 360: Impact and Design. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (AAAI, 2022)

Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations. - Yair Schiff; Vijil Chenthamarakshan; Samuel C. Hoffman; Karthikeyan Natesan Ramamurthy; Payel Das (ICASSP, 2022)

An Empirical Study of Modular Bias Mitigators and Ensembles. - Michael Feffer; Martin Hirzel; Samuel C. Hoffman; Kiran Kate; Parikshit Ram; Avraham Shinnar (CoRR, 2022)

Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework. - Vijil Chenthamarakshan; Samuel C. Hoffman; C. David Owen; Petra Lukacik; Claire Strain-Damerell; Daren Fearon; Tika R. Malla; Anthony Tumber; Christopher J. Schofield; Helen M. E. Duyvesteyn; Wanwisa Dejnirattisai; Loic Carrique; et al. (CoRR, 2022)

GT4SD: Generative Toolkit for Scientific Discovery. - Matteo Manica; Joris Cadow; Dimitrios Christofidellis; Ashish Dave; Jannis Born; Dean Clarke; Yves Gaetan Nana Teukam; Samuel C. Hoffman; Matthew Buchan; Vijil Chenthamarakshan; Timothy Donovan; Hsiang-Han Hsu; et al. (CoRR, 2022)

Causal Graphs Underlying Generative Models: Path to Learning with Limited Data. - Samuel C. Hoffman; Kahini Wadhawan; Payel Das; Prasanna Sattigeri; Karthikeyan Shanmugam (CoRR, 2022)

Navigating Ensemble Configurations for Algorithmic Fairness. - Michael Feffer; Martin Hirzel; Samuel C. Hoffman; Kiran Kate; Parikshit Ram; Avraham Shinnar (CoRR, 2022)

Optimizing molecules using efficient queries from property evaluations. - Samuel C. Hoffman; Vijil Chenthamarakshan; Kahini Wadhawan; Pin-Yu Chen; Payel Das (Nat. Mach. Intell., 2022)

Searching for Fairer Machine Learning Ensembles. - Michael Feffer; Martin Hirzel; Samuel C. Hoffman; Kiran Kate; Parikshit Ram; Avraham Shinnar (AutoML, 2023)

Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions. - Manish Nagireddy; Moninder Singh; Samuel C. Hoffman; Evaline Ju; Karthikeyan Natesan Ramamurthy; Kush R. Varshney (CoRR, 2023)

GP-MoLFormer: A Foundation Model For Molecular Generation. - Jerret Ross; Brian Belgodere; Samuel C. Hoffman; Vijil Chenthamarakshan; Youssef Mroueh; Payel Das (CoRR, 2024)

Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data. - Samuel C. Hoffman; Payel Das; Karthikeyan Shanmugam; Kahini Wadhawan; Prasanna Sattigeri (Trans. Mach. Learn. Res., 2024)

GP-MoLFormer-Sim: Test Time Molecular Optimization through Contextual Similarity Guidance. - Jirí Navrátil 001; Jarret Ross; Payel Das; Youssef Mroueh; Samuel C. Hoffman; Vijil Chenthamarakshan; Brian Belgodere (CoRR, 2025)