Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDKush R. Varshney
Person information
- affiliation: IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA
- affiliation (former): Massachusetts Institute of Technology, Cambridge, MA, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[c89]Kush R. Varshney, Lav R. Varshney:
A Banal Account of a Safety-Creativity Tradeoff in Generative AI 163-165. IUI Workshops 2023: 163-165- [i80]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) - [i79]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 abs/2303.02343 (2023) - 2022
- [j42]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): 83:1-83:22 (2022) - [j41]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): 100493 (2022) - [c88]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: 12651-12657 - [c87]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: 333-335 - [c86]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: 1-4 - [c85]Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney:
Causal Feature Selection for Algorithmic Fairness. SIGMOD Conference 2022: 276-285 - [c84]Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R. Varshney, Siwei Lyu, Yiming Ying:
Differentially private SGDA for minimax problems. UAI 2022: 2192-2202 - [i78]Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R. Varshney, Siwei Lyu, Yiming Ying:
Differentially Private SGDA for Minimax Problems. CoRR abs/2201.09046 (2022) - [i77]Bran Knowles, Jason D'Cruz, John T. Richards, Kush R. Varshney:
Humble Machines: Attending to the Underappreciated Costs of Misplaced Distrust. CoRR abs/2208.01305 (2022) - [i76]Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying:
Minimax AUC Fairness: Efficient Algorithm with Provable Convergence. CoRR abs/2208.10451 (2022) - [i75]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 abs/2210.06475 (2022) - [i74]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 abs/2211.01498 (2022) - [i73]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 abs/2212.06803 (2022) - 2021
- [j40]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): 47-58 (2021) - [j39]Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri
, Kush R. Varshney:
Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes. Entropy 23(12): 1571 (2021) - [j38]Lu Cheng, Kush R. Varshney, Huan Liu:
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges. J. Artif. Intell. Res. 71: 1137-1181 (2021) - [c83]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: 15988-15990 - [c82]Diego García-Olano, Yasumasa Onoe, Ioana Baldini, Joydeep Ghosh, Byron C. Wallace, Kush R. Varshney:
Biomedical Interpretable Entity Representations. ACL/IJCNLP (Findings) 2021: 3547-3561 - [c81]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: 346-356 - [c80]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 - [c79]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: 376-379 - [c78]Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar:
Treatment Effect Estimation Using Invariant Risk Minimization. ICASSP 2021: 5005-5009 - [c77]Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney:
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. ICLR 2021 - [c76]Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney:
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. NeurIPS 2021: 21668-21680 - [c75]Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind:
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users. PERSUASIVE 2021: 135-149 - [i72]Lu Cheng, Kush R. Varshney, Huan Liu:
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges. CoRR abs/2101.02032 (2021) - [i71]Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind:
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users. CoRR abs/2101.12715 (2021) - [i70]Yu Tao, Kush R. Varshney:
Insiders and Outsiders in Research on Machine Learning and Society. CoRR abs/2102.02279 (2021) - [i69]Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar:
Treatment Effect Estimation using Invariant Risk Minimization. CoRR abs/2103.07788 (2021) - [i68]Lu Cheng, Dmitriy A. Katz-Rogozhnikov, Kush R. Varshney, Ioana Baldini:
Automated Meta-Analysis: A Causal Learning Perspective. CoRR abs/2104.04633 (2021) - [i67]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 abs/2105.11160 (2021) - [i66]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 abs/2106.01410 (2021) - [i65]Diego García-Olano, Yasumasa Onoe, Ioana Baldini, Joydeep Ghosh, Byron C. Wallace, Kush R. Varshney:
Biomedical Interpretable Entity Representations. CoRR abs/2106.09502 (2021) - [i64]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 abs/2108.08077 (2021) - [i63]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) - [i62]Moninder Singh, Gevorg Ghalachyan, Kush R. Varshney, Reginald E. Bryant:
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness. CoRR abs/2109.14653 (2021) - [i61]Q. Vera Liao, Kush R. Varshney:
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences. CoRR abs/2110.10790 (2021) - 2020
- [j37]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: 130:1-130:6 (2020) - [c74]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: 41-53 - [c73]Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush R. Varshney, Dharmashankar Subramanian:
Event-Driven Continuous Time Bayesian Networks. AAAI 2020: 3259-3266 - [c72]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: 13369-13381 - [c71]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: 358-364 - [c70]Yunfeng Zhang, Rachel K. E. Bellamy, Kush R. Varshney:
Joint Optimization of AI Fairness and Utility: A Human-Centered Approach. AIES 2020: 400-406 - [c69]Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney:
Characterization of Overlap in Observational Studies. AISTATS 2020: 788-798 - [c68]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 - [c67]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: 1-8 - [c66]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: 19-29 - [c65]Kush R. Varshney:
On Mismatched Detection and Safe, Trustworthy Machine Learning. CISS 2020: 1-4 - [c64]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: 696 - [c63]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: 3627-3631 - [c62]Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar:
Invariant Risk Minimization Games. ICML 2020: 145-155 - [c61]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: 2803-2813 - [c60]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: 5282-5284 - [c59]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: 3547-3548 - [c58]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: 320-329 - [i60]Yunfeng Zhang, Rachel K. E. Bellamy, Kush R. Varshney:
Joint Optimization of AI Fairness and Utility: A Human-Centered Approach. CoRR abs/2002.01621 (2020) - [i59]Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar:
Invariant Risk Minimization Games. CoRR abs/2002.04692 (2020) - [i58]Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney:
Fair Data Integration. CoRR abs/2006.06053 (2020) - [i57]Stacy Hobson, Michael Hind, Aleksandra Mojsilovic, Kush R. Varshney:
Trust and Transparency in Contact Tracing Applications. CoRR abs/2006.11356 (2020) - [i56]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 abs/2010.07938 (2020) - [i55]Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney:
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. CoRR abs/2010.16412 (2020) - [i54]Kartik Ahuja, Amit Dhurandhar, Kush R. Varshney:
Learning to Initialize Gradient Descent Using Gradient Descent. CoRR abs/2012.12141 (2020)
2010 – 2019
- 2019
- [j36]Kush R. Varshney:
Trustworthy machine learning and artificial intelligence. XRDS 25(3): 26-29 (2019) - [j35]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): 7:1-7:18 (2019) - [j34]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. IBM J. Res. Dev. 63(4/5): 2:1-2:9 (2019) - [j33]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) - [j32]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 and mitigating algorithmic bias. IBM J. Res. Dev. 63(4/5): 4:1-4:15 (2019) - [j31]Matthew Arnold, Rachel K. E. Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, David Piorkowski, Darrell Reimer, John T. Richards, Jason Tsay, Kush R. Varshney:
FactSheets: Increasing trust in AI services through supplier's declarations of conformity. IBM J. Res. Dev. 63(4/5): 6:1-6:13 (2019) - [j30]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): 237:1-237:23 (2019) - [j29]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): 76-80 (2019) - [c57]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: 91-98 - [c56]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: 123-129 - [c55]Pranay Kr. Lohia, Karthikeyan Natesan Ramamurthy, Manish Bhide, Diptikalyan Saha, Kush R. Varshney, Ruchir Puri:
Bias Mitigation Post-processing for Individual and Group Fairness. ICASSP 2019: 2847-2851 - [c54]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: 7500-7504 - [c53]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: 277-284 - [c52]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: 311-319 - [c51]Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Krishnan Mody:
Topological Data Analysis of Decision Boundaries with Application to Model Selection. ICML 2019: 5351-5360 - [c50]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: 6377-6381 - [i53]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 abs/1905.03297 (2019) - [i52]Kush R. Varshney, Aleksandra Mojsilovic:
Open Platforms for Artificial Intelligence for Social Good: Common Patterns as a Pathway to True Impact. CoRR abs/1905.11519 (2019) - [i51]Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic:
Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning. CoRR abs/1906.02299 (2019) - [i50]Fredrik D. Johansson, Dennis Wei, Michael Oberst, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney:
Characterization of Overlap in Observational Studies. CoRR abs/1907.04138 (2019) - [i49]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) - [i48]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 abs/1909.03486 (2019) - [i47]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 abs/1910.07870 (2019) - [i46]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 abs/1910.13268 (2019) - [i45]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 abs/1910.13983 (2019) - [i44]Samuel C. Maina, Reginald E. Bryant, William O. Goal, Robert-Florian Samoilescu, Kush R. Varshney, Komminist Weldemariam:
Preservation of Anomalous Subgroups On Machine Learning Transformed Data. CoRR abs/1911.03674 (2019) - [i43]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 abs/1911.07819 (2019) - [i42]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 abs/1911.08293 (2019) - 2018
- [j28]Flávio du Pin Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Data Pre-Processing for Discrimination Prevention: Information-Theoretic Optimization and Analysis. IEEE J. Sel. Top. Signal Process. 12(5): 1106-1119 (2018) - [j27]Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Distribution-preserving k-anonymity. Stat. Anal. Data Min. 11(6): 253-270 (2018) - [c49]Jonathan Galsurkar, Moninder Singh, Lingfei Wu, Aditya Vempaty, Mikhail Sushkov, Devika Iyer, Serge Kapto, Kush R. Varshney:
Assessing National Development Plans for Alignment With Sustainable Development Goals via Semantic Search. AAAI 2018: 7753-7758 - [c48]Bhanukiran Vinzamuri, Kush R. Varshney:
False Discovery Rate Control with Concave Penalties Using Stability Selection. DSW 2018: 76-80 - [c47]Alexandra Olteanu, Carlos Castillo, Jeremy Boy, Kush R. Varshney:
The Effect of Extremist Violence on Hateful Speech Online. ICWSM 2018: 221-230 - [c46]Evan Patterson, Ioana Baldini, Aleksandra Mojsilovic, Kush R. Varshney:
Semantic Representation of Data Science Programs. IJCAI 2018: 5847-5849 - [r2]Jun Wang, Kush R. Varshney, Aleksandra Mojsilovic:
Legislative Prediction with Political and Social Network Analysis. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018 - [i41]Kush R. Varshney:
How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis. CoRR abs/1803.11261 (2018) - [i40]Alexandra Olteanu, Carlos Castillo, Jeremy Boy, Kush R. Varshney:
The Effect of Extremist Violence on Hateful Speech Online. CoRR abs/1804.05704 (2018) - [i39]Bernat Guillen Pegueroles, Bhanukiran Vinzamuri, Karthikeyan Shanmugam, Steve Hedden, Jonathan D. Moyer, Kush R. Varshney:
Structure Learning from Time Series with False Discovery Control. CoRR abs/1805.09909 (2018) - [i38]Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney:
Fairness GAN. CoRR abs/1805.09910 (2018) - [i37]Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Krishnan Mody:
Topological Data Analysis of Decision Boundaries with Application to Model Selection. CoRR abs/1805.09949 (2018) - [i36]Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic:
Teaching Meaningful Explanations. CoRR abs/1805.11648 (2018) - [i35]Kush R. Varshney, Prashant Khanduri, Pranay Sharma, Shan Zhang, Pramod K. Varshney:
Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory. CoRR abs/1806.09710 (2018) - [i34]Been Kim, Kush R. Varshney, Adrian Weller:
Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018). CoRR abs/1807.01308 (2018) - [i33]Evan Patterson, Ioana Baldini, Aleksandra Mojsilovic, Kush R. Varshney:
Teaching machines to understand data science code by semantic enrichment of dataflow graphs. CoRR abs/1807.05691 (2018) - [i32]Michael Hind, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, Kush R. Varshney:
Increasing Trust in AI Services through Supplier's Declarations of Conformity. CoRR abs/1808.07261 (2018) - [i31]Ritesh Noothigattu, Djallel Bouneffouf, Nicholas Mattei, Rachita Chandra, Piyush Madan, Kush R. Varshney, Murray Campbell, Moninder Singh, Francesca Rossi:
Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration. CoRR abs/1809.08343 (2018) - [i30]