Kush R. Varshney

ORCID

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 176

Top Venues

Massachusetts Institute of Technology, Cambridge, MA, USA (PHD)

Education History

Massachusetts Institute of Technology, Cambridge, MA, USA

PHD

Publications

William Kidder, Jason D'Cruz, Kush R. Varshney
Empathy and the Right to Be an Exception: What LLMs Can and Cannot Do. CoRR (2024)
Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Xiaojun Xu, Yuguang Yao, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, Yang Liu
Rethinking Machine Unlearning for Large Language Models. CoRR (2024)
Yihua Zhang, Pranay Sharma, Parikshit Ram, Mingyi Hong, Kush R. Varshney, Sijia Liu
What Is Missing in IRM Training and Evaluation? Challenges and Solutions. ICLR (2023)
Yihua Zhang, Pranay Sharma, Parikshit Ram, Mingyi Hong, Kush R. Varshney, Sijia Liu
What Is Missing in IRM Training and Evaluation? Challenges and Solutions. CoRR (2023)
Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Vijil Chenthamarakshan, Kush R. Varshney, Lav R. Varshney, Payel Das
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models. AAAI (2023)
Girmaw Abebe Tadesse, Celia Cintas, Kush R. Varshney, Peter W. J. Staar, Chinyere Agunwa, Skyler Speakman, Justin Jia, Elizabeth E. Bailey, Ademide Adelekun, Jules Lipoff, Ginikanwa Onyekaba, Jenna C. Lester, Veronica Rotemberg, James Zou, Roxana Daneshjou
Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning. npj Digit. Medicine 6 (2023)
Baihan Lin, Djallel Bouneffouf, Guillermo A. Cecchi, Kush R. Varshney
Towards Healthy AI: Large Language Models Need Therapists Too. CoRR (2023)
Ioana Baldini, Chhavi Yadav, Payel Das, Kush R. Varshney
Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models. CoRR (2023)
Kush R. Varshney, Lav R. Varshney
A Banal Account of a Safety-Creativity Tradeoff in Generative AI 163-165. IUI Workshops (2023)
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 (2023)
Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying
Minimax AUC Fairness: Efficient Algorithm with Provable Convergence. AAAI (2023)
Kush R. Varshney
Decolonial AI Alignment: Viśesadharma, Argument, and Artistic Expression. CoRR (2023)
Katy Ilonka Gero, Payel Das, Pierre L. Dognin, Inkit Padhi, Prasanna Sattigeri, Kush R. Varshney
The incentive gap in data work in the era of large models. Nat. Mac. Intell. 5 (6) (2023)
Bran Knowles, Jason D'Cruz, John T. Richards, Kush R. Varshney
Humble AI. Commun. ACM 66 (9) (2023)
Bran Knowles, Jasmine Fledderjohann, John T. Richards, Kush R. Varshney
Trustworthy AI and the Logics of Intersectional Resistance. FAccT (2023)
Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy, Jirí Navrátil, Prasanna Sattigeri, Kush R. Varshney, Yunfeng Zhang
Uncertainty Quantification 360: A Hands-on Tutorial. COMAD/CODS (2022)
Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth M. Daly, Moninder Singh
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. CoRR (2022)
Sanjoy Dey, Prithwish Chakraborty, Bum Chul Kwon, Amit Dhurandhar, Mohamed F. Ghalwash, Fernando J. Suarez Saiz, Kenney Ng, Daby Sow, Kush R. Varshney, Pablo Meyer
Human-centered explainability for life sciences, healthcare, and medical informatics. Patterns 3 (5) (2022)
Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Vijil Chenthamarakshan, Kush R. Varshney, Lav R. Varshney, Payel Das
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models. CoRR (2022)
Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth Daly, Moninder Singh
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. NeurIPS (2022)
Prasanna Sattigeri, Soumya Ghosh, Inkit Padhi, Pierre L. Dognin, Kush R. Varshney
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting. CoRR (2022)
Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R. Varshney, Siwei Lyu, Yiming Ying
Differentially Private SGDA for Minimax Problems. CoRR (2022)
Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney
Causal Feature Selection for Algorithmic Fairness. SIGMOD Conference (2022)
Hannah Kim, Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman, Kush R. Varshney
Out-of-Distribution Detection in Dermatology Using Input Perturbation and Subset Scanning. ISBI (2022)
Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R. Varshney, Siwei Lyu, Yiming Ying
Differentially private SGDA for minimax problems. UAI (2022)
Prasanna Sattigeri, Soumya Ghosh, Inkit Padhi, Pierre L. Dognin, Kush R. Varshney
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting. NeurIPS (2022)
Bran Knowles, Jason D'Cruz, John T. Richards, Kush R. Varshney
Humble Machines: Attending to the Underappreciated Costs of Misplaced Distrust. CoRR (2022)
Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying
Minimax AUC Fairness: Efficient Algorithm with Provable Convergence. CoRR (2022)
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. AAAI (2022)
Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. Proc. ACM Hum. Comput. Interact. 6 (CSCW1) (2022)
Ioana Baldini, Mariana Bernagozzi, Sulbha Aggarwal, Mihaela A. Bornea, Saksham Chawla, Joppe Geluykens, Dmitriy A. Katz-Rogozhnikov, Pratik Mukherjee, Smruthi Ramesh, Sara Rosenthal, Jagrati Sharma, Kush R. Varshney, Laura B. Kleiman, Pradeep Mangalath, Catherine Del Vecchio Fitz
Exploring the Efficacy of Generic Drugs in Treating Cancer. AAAI (2021)
Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. ICLR (2021)
Lu Cheng, Kush R. Varshney, Huan Liu
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges. J. Artif. Intell. Res. 71 (2021)
Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney
Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes. Entropy 23 (12) (2021)
Q. Vera Liao, Kush R. Varshney
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences. CoRR (2021)
Kahini Wadhawan, Payel Das, Barbara A. Han, Ilya R. Fischhoff, Adrian C. Castellanos, Arvind Varsani, Kush R. Varshney
Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention. CoRR (2021)
Lu Cheng, Kush R. Varshney, Huan Liu
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges. CoRR (2021)
Yu Tao, Kush R. Varshney
Insiders and Outsiders in Research on Machine Learning and Society. CoRR (2021)
Jason D'Cruz, William Kidder, Kush R. Varshney
The Empathy Gap: Why AI Can Forecast Behavior But Cannot Assess Trustworthiness. TFSOCTAI@AAAI Fall Symposium (2021)
Lu Cheng, Dmitriy A. Katz-Rogozhnikov, Kush R. Varshney, Ioana Baldini
Automated Meta-Analysis: A Causal Learning Perspective. CoRR (2021)
Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar
Treatment Effect Estimation using Invariant Risk Minimization. CoRR (2021)
Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy, Jirí Navrátil, Prasanna Sattigeri, Kush R. Varshney, Yunfeng Zhang
Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI. CoRR (2021)
Hannah Kim, Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman, Kush R. Varshney
Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning. CoRR (2021)
Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users. CoRR (2021)
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 Toolkit. COMAD/CODS (2021)
Michiel A. Bakker, Duy Patrick Tu, Krishna P. Gummadi, Alex 'Sandy' Pentland, Kush R. Varshney, Adrian Weller
Beyond Reasonable Doubt: Improving Fairness in Budget-Constrained Decision Making using Confidence Thresholds. AIES (2021)
John T. Richards, David Piorkowski, Michael Hind, Stephanie Houde, Aleksandra Mojsilovic, Kush R. Varshney
A Human-Centered Methodology for Creating AI FactSheets. IEEE Data Eng. Bull. 44 (4) (2021)
Diego García-Olano, Yasumasa Onoe, Ioana Baldini, Joydeep Ghosh, Byron C. Wallace, Kush R. Varshney
Biomedical Interpretable Entity Representations. CoRR (2021)
Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users. PERSUASIVE (2021)
Diego García-Olano, Yasumasa Onoe, Ioana Baldini, Joydeep Ghosh, Byron C. Wallace, Kush R. Varshney
Biomedical Interpretable Entity Representations. ACL/IJCNLP (Findings) (2021)
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 (2021)
Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar
Treatment Effect Estimation Using Invariant Risk Minimization. ICASSP (2021)
Moninder Singh, Gevorg Ghalachyan, Kush R. Varshney, Reginald E. Bryant
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness. CoRR (2021)
Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. NeurIPS (2021)
Girmaw Abebe Tadesse, Celia Cintas, Roxana Daneshjou, Kush R. Varshney, Peter Staar, Skyler Speakman, Kenya Andrews, Chinyere Agunwa, Justin Jia, Elizabeth E. Bailey, Jules Lipoff, Ginikanwa Onyekaba, Veronica Rotemberg, Ademide Adelekun, James Y. Zou
Racial Representation Analysis in Dermatology Academic Materials. AMIA (2021)
Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. CoRR (2020)
Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar
Invariant Risk Minimization Games. ICML (2020)
Shivashankar Subramanian, Ioana Baldini, Sushma Ravichandran, Dmitriy A. Katz-Rogozhnikov, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Kush R. Varshney, Annmarie Wang, Pradeep Mangalath, Laura B. Kleiman
A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications. AAAI (2020)
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: An Extensible Toolkit for Understanding Data and Machine Learning Models. J. Mach. Learn. Res. 21 (2020)
Newton M. Kinyanjui, Timothy Odonga, Celia Cintas, Noel C. F. Codella, Rameswar Panda, Prasanna Sattigeri, Kush R. Varshney
Fairness of Classifiers Across Skin Tones in Dermatology. MICCAI (6) (2020)
Stacy Hobson, Michael Hind, Aleksandra Mojsilovic, Kush R. Varshney
Trust and Transparency in Contact Tracing Applications. CoRR (2020)
Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David A. Sontag, Kush R. Varshney
Characterization of Overlap in Observational Studies. AISTATS (2020)
Kush R. Varshney
On Mismatched Detection and Safe, Trustworthy Machine Learning. CISS (2020)
Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri, Monica Shekhar, Sara E. Berger, Subhro Das, Kush R. Varshney
Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines. CHIL (2020)
Michiel A. Bakker, Humberto Riverón Valdés, Duy Patrick Tu, Krishna P. Gummadi, Kush R. Varshney, Adrian Weller, Alex Pentland
Fair Enough: Improving Fairness in Budget-Constrained Decision Making Using Confidence Thresholds. SafeAI@AAAI (2020)
William Ogallo, Skyler Speakman, Victor Akinwande, Kush R. Varshney, Aisha Walcott, Charity Wayua, Komminist Weldemariam, Claire-Helene Mershon, Nosa Orobaton
Identifying Factors Associated with Neonatal Mortality in Sub-Saharan Africa using Machine Learning. AMIA (2020)
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: hands-on tutorial. FAT* (2020)
Shubham Sharma, Yunfeng Zhang, Jesús M. Ríos Aliaga, Djallel Bouneffouf, Vinod Muthusamy, Kush R. Varshney
Data Augmentation for Discrimination Prevention and Bias Disambiguation. AIES (2020)
William Ogallo, Skyler Speakman, Victor Akinwande, Kush R. Varshney, Aisha Walcott-Bryant, Charity Wayua, Komminist Weldemariam
Inspection of Blackbox Models for Evaluating Vulnerability in Maternal, Newborn, and Child Health. IJCAI (2020)
Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney
Fair Data Integration. CoRR (2020)
Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush R. Varshney
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing. ICML (2020)
Michael Hind, Stephanie Houde, Jacquelyn Martino, Aleksandra Mojsilovic, David Piorkowski, John T. Richards, Kush R. Varshney
Experiences with Improving the Transparency of AI Models and Services. CHI Extended Abstracts (2020)
Kartik Ahuja, Amit Dhurandhar, Kush R. Varshney
Learning to Initialize Gradient Descent Using Gradient Descent. CoRR (2020)
Samuel C. Maina, Reginald E. Bryant, William O. Ogallo, Kush R. Varshney, Skyler Speakman, Celia Cintas, Aisha Walcott-Bryant, Robert-Florian Samoilescu, Komminist Weldemariam
Preservation of Anomalous Subgroups On Variational Autoencoder Transformed Data. ICASSP (2020)
Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush R. Varshney, Dharmashankar Subramanian
Event-Driven Continuous Time Bayesian Networks. AAAI (2020)
Yunfeng Zhang, Rachel K. E. Bellamy, Kush R. Varshney
Joint Optimization of AI Fairness and Utility: A Human-Centered Approach. AIES (2020)
Prithwish Chakraborty, Bum Chul Kwon, Sanjoy Dey, Amit Dhurandhar, Daniel Gruen, Kenney Ng, Daby Sow, Kush R. Varshney
Tutorial on Human-Centered Explainability for Healthcare. KDD (2020)
Yunfeng Zhang, Rachel K. E. Bellamy, Kush R. Varshney
Joint Optimization of AI Fairness and Utility: A Human-Centered Approach. CoRR (2020)
Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. CoRR (2020)
Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar
Invariant Risk Minimization Games. CoRR (2020)
Newton M. Kinyanjui, Timothy Odonga, Celia Cintas, Noel C. F. Codella, Rameswar Panda, Prasanna Sattigeri, Kush R. Varshney
Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets. CoRR (2019)
Michiel A. Bakker, Duy Patrick Tu, Humberto Riverón Valdés, Krishna P. Gummadi, Kush R. Varshney, Adrian Weller, Alex Pentland
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning. CoRR (2019)
Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilovic, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
TED: Teaching AI to Explain its Decisions. AIES (2019)
Ritesh Noothigattu, Djallel Bouneffouf, Nicholas Mattei, Rachita Chandra, Piyush Madan, Kush R. Varshney, Murray Campbell, Moninder Singh, Francesca Rossi
Teaching AI Agents Ethical Values Using Reinforcement Learning and Policy Orchestration. IJCAI (2019)
Nelson Kibichii Bore, Ravi Kiran Raman, Isaac M. Markus, Sekou L. Remy, Oliver Bent, Michael Hind, Eleftheria Kyriaki Pissadaki, Biplav Srivastava, Roman Vaculín, Kush R. Varshney, Komminist Weldemariam
Promoting Distributed Trust in Machine Learning and Computational Simulation. IEEE ICBC (2019)
Michael Hind, Stephanie Houde, Jacquelyn Martino, Aleksandra Mojsilovic, David Piorkowski, John T. Richards, Kush R. Varshney
Experiences with Improving the Transparency of AI Models and Services. CoRR (2019)
Ravi Kiran Raman, Kush R. Varshney, Roman Vaculín, Nelson Kibichii Bore, Sekou L. Remy, Eleftheria Kyriaki Pissadaki, Michael Hind
Constructing and Compressing Frames in Blockchain-based Verifiable Multi-party Computation. ICASSP (2019)
Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Krishnan Mody
Topological Data Analysis of Decision Boundaries with Application to Model Selection. ICML (2019)
Shivashankar Subramanian, Ioana Baldini, Sushma Ravichandran, Dmitriy A. Katz-Rogozhnikov, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Kush R. Varshney, Annmarie Wang, Pradeep Mangalath, Laura B. Kleiman
Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies. CoRR (2019)
Yaoli Mao, Dakuo Wang, Michael J. Muller, Kush R. Varshney, Ioana Baldini, Casey Dugan, Aleksandra Mojsilovic
How Data ScientistsWork Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question? Proc. ACM Hum. Comput. Interact. 3 (GROUP) (2019)
Kush R. Varshney, Aleksandra Mojsilovic
Open Platforms for Artificial Intelligence for Social Good: Common Patterns as a Pathway to True Impact. CoRR (2019)
Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri, Monica Shekhar, Sara E. Berger, Subhro Das, Kush R. Varshney
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines. CoRR (2019)
Ravi Kiran Raman, Roman Vaculín, Michael Hind, Sekou L. Remy, Eleftheria Kyriaki Pissadaki, Nelson Kibichii Bore, Roozbeh Daneshvar, Biplav Srivastava, Kush R. Varshney
A Scalable Blockchain Approach for Trusted Computation and Verifiable Simulation in Multi-Party Collaborations. IEEE ICBC (2019)
Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush R. Varshney
An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy. CoRR (2019)
Yaoli Mao, Dakuo Wang, Michael J. Muller, Kush R. Varshney, Ioana Baldini, Casey Dugan, Aleksandra Mojsilovic
How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question? CoRR (2019)
Lav R. Varshney, Florian Pinel, Kush R. Varshney, Debarun Bhattacharjya, Angela Schörgendorfer, Yi-Min Chee
A big data approach to computational creativity: The curious case of Chef Watson. IBM J. Res. Dev. 63 (1) (2019)
Kush R. Varshney
Trustworthy machine learning and artificial intelligence. XRDS 25 (3) (2019)
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) (2019)
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, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang
Think Your Artificial Intelligence Software Is Fair? Think Again. IEEE Softw. 36 (4) (2019)
Amanda Coston, Karthikeyan Natesan Ramamurthy, Dennis Wei, Kush R. Varshney, Skyler Speakman, Zairah Mustahsan, Supriyo Chakraborty
Fair Transfer Learning with Missing Protected Attributes. AIES (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Kush R. Varshney