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Samuel C. Hoffman
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2020 – today
- 2024
- [i16]Jerret Ross, Brian Belgodere, Samuel C. Hoffman, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das:
GP-MoLFormer: A Foundation Model For Molecular Generation. CoRR abs/2405.04912 (2024) - 2023
- [c7]Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar:
Searching for Fairer Machine Learning Ensembles. AutoML 2023: 17/1-19 - [i15]Manish Nagireddy, Moninder Singh, Samuel C. Hoffman, Evaline Ju, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions. CoRR abs/2302.09190 (2023) - 2022
- [j5]Samuel C. Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan
, Pin-Yu Chen
Optimizing molecules using efficient queries from property evaluations. Nat. Mach. Intell. 4(1): 21-31 (2022) - [c6]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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam
AI Explainability 360: Impact and Design. AAAI 2022: 12651-12657 - [c5]Yair Schiff, Vijil Chenthamarakshan, Samuel C. Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das:
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations. ICASSP 2022: 3783-3787 - [i14]Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar:
An Empirical Study of Modular Bias Mitigators and Ensembles. CoRR abs/2202.00751 (2022) - [i13]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, Thomas S. Walter, Gavin R. Screaton, Tetiana Matviiuk, Aleksandra Mojsilovic, Jason Crain, Martin A. Walsh, David I. Stuart, Payel Das:
Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework. CoRR abs/2204.09042 (2022) - [i12]Matteo Manica, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Jannis Born, Dean Clarke, Yves Gaetan Nana Teukam, Samuel C. Hoffman, Matthew Buchan
GT4SD: Generative Toolkit for Scientific Discovery. CoRR abs/2207.03928 (2022) - [i11]Samuel C. Hoffman, Kahini Wadhawan, Payel Das, Prasanna Sattigeri, Karthikeyan Shanmugam:
Causal Graphs Underlying Generative Models: Path to Learning with Limited Data. CoRR abs/2207.07174 (2022) - [i10]Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar:
Navigating Ensemble Configurations for Algorithmic Fairness. CoRR abs/2210.05594 (2022) - 2021
- [c4]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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam
AI Explainability 360 Toolkit. COMAD/CODS 2021: 376-379 - [i9]Yair Schiff, Vijil Chenthamarakshan, Samuel C. Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das:
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations. CoRR abs/2106.04464 (2021) - [i8]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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
AI Explainability 360: Impact and Design. CoRR abs/2109.12151 (2021) - [i7]Samuel C. Hoffman, Vijil Chenthamarakshan, Dmitry Yu. Zubarev, Daniel P. Sanders, Payel Das:
Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model. CoRR abs/2112.01625 (2021) - 2020
- [j4]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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. J. Mach. Learn. Res. 21: 130:1-130:6 (2020) - [c3]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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam
AI explainability 360: hands-on tutorial. FAT* 2020: 696 - [c2]Hamid Dadkhahi, Karthikeyan Shanmugam
Combinatorial Black-Box Optimization with Expert Advice. KDD 2020: 1918-1927 - [c1]Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic:
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models. NeurIPS 2020 - [i6]Vijil Chenthamarakshan, Payel Das, Inkit Padhi, Hendrik Strobelt, Kar Wai Lim, Benjamin Hoover, Samuel C. Hoffman, Aleksandra Mojsilovic:
Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models. CoRR abs/2004.01215 (2020) - [i5]Hamid Dadkhahi, Karthikeyan Shanmugam, Jesus Rios, Payel Das, Samuel C. Hoffman, Troy David Loeffler, Subramanian Sankaranarayanan:
Combinatorial Black-Box Optimization with Expert Advice. CoRR abs/2006.03963 (2020) - [i4]Samuel C. Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, Payel Das:
Optimizing Molecules using Efficient Queries from Property Evaluations. CoRR abs/2011.01921 (2020)
2010 – 2019
- 2019
- [j3]Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney:
Fairness GAN: Generating datasets with fairness properties using a generative adversarial network. IBM J. Res. Dev. 63(4/5): 3:1-3:9 (2019) - [j2]Rachel K. E. Bellamy
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM J. Res. Dev. 63(4/5): 4:1-4:15 (2019) - [j1]Rachel K. E. Bellamy
Think Your Artificial Intelligence Software Is Fair? Think Again. IEEE Softw. 36(4): 76-80 (2019) - [i3]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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. CoRR abs/1909.03012 (2019) - 2018
- [i2]Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney:
Fairness GAN. CoRR abs/1805.09910 (2018) - [i1]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, John T. Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang:
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias. CoRR abs/1810.01943 (2018)
Coauthor Index
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