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Kush R. Varshney
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- affiliation: IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA
- affiliation (former): Massachusetts Institute of Technology, Cambridge, MA, USA
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2020 – today
- 2024
- [j46]Swapnaja Achintalwar
, Ioana Baldini
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations. IEEE Internet Comput. 28(5): 28-36 (2024) - [c101]Inkit Padhi, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Manish Nagireddy, Pierre L. Dognin, Kush R. Varshney:
Value Alignment from Unstructured Text. EMNLP (Industry Track) 2024: 1083-1095 - [c100]Victor Akinwande, Megan MacGregor, Celia Cintas, Ehud Karavani, Dennis Wei, Kush R. Varshney, Pablo A. Nepomnaschy:
Using Causal Inference to Investigate Contraceptive Discontinuation in Sub-Saharan Africa. IJCAI 2024: 7161-7169 - [c99]Inkit Padhi, Pierre L. Dognin, Jesus Rios, Ronny Luss, Swapnaja Achintalwar, Matthew Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, Djallel Bouneffouf:
ComVas: Contextual Moral Values Alignment System. IJCAI 2024: 8759-8762 - [c98]Assala Benmalek, Celia Cintas, Girmaw Abebe Tadesse, Roxana Daneshjou, Kush R. Varshney, Dalila Cherifi:
Evaluating the Impact of Skin Tone Representation on Out-of-Distribution Detection Performance in Dermatology. ISBI 2024: 1-5 - [i94]William Kidder, Jason D'Cruz, Kush R. Varshney:
Empathy and the Right to Be an Exception: What LLMs Can and Cannot Do. CoRR abs/2401.14523 (2024) - [i93]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 abs/2402.08787 (2024) - [i92]Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E. Berger
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations. CoRR abs/2403.06009 (2024) - [i91]Swapnaja Achintalwar, Ioana Baldini, Djallel Bouneffouf, Joan Byamugisha, Maria Chang, Pierre L. Dognin, Eitan Farchi, Ndivhuwo Makondo, Aleksandra Mojsilovic, Manish Nagireddy, Karthikeyan Natesan Ramamurthy, Inkit Padhi, Orna Raz, Jesus Rios, Prasanna Sattigeri, Moninder Singh, Siphiwe Thwala, Rosario A. Uceda-Sosa, Kush R. Varshney:
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations. CoRR abs/2403.09704 (2024) - [i90]Conor M. Artman, Aditya Mate, Ezinne Nwankwo, Aliza Heching, Tsuyoshi Idé, Jirí Navrátil, Karthikeyan Shanmugam, Wei Sun, Kush R. Varshney, Lauri Goldkind, Gidi Kroch, Jaclyn Sawyer, Ian Watson:
A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food. CoRR abs/2403.10638 (2024) - [i89]Pierre L. Dognin, Jesus Rios, Ronny Luss, Inkit Padhi, Matthew D. Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, Djallel Bouneffouf:
Contextual Moral Value Alignment Through Context-Based Aggregation. CoRR abs/2403.12805 (2024) - [i88]Emmie Malone, Saleh Afroogh, Jason D'Cruz, Kush R. Varshney:
When Trust is Zero Sum: Automation Threat to Epistemic Agency. CoRR abs/2408.08846 (2024) - [i87]Inkit Padhi, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Manish Nagireddy, Pierre L. Dognin, Kush R. Varshney:
Value Alignment from Unstructured Text. CoRR abs/2408.10392 (2024) - [i86]Ambrish Rawat, Stefan Schoepf, Giulio Zizzo, Giandomenico Cornacchia, Muhammad Zaid Hameed, Kieran Fraser, Erik Miehling, Beat Buesser, Elizabeth M. Daly, Mark Purcell, Prasanna Sattigeri, Pin-Yu Chen, Kush R. Varshney:
Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI. CoRR abs/2409.15398 (2024) - [i85]Hangzhi Guo, Pranav Narayanan Venkit, Eunchae Jang, Mukund Srinath, Wenbo Zhang, Bonam Mingole, Vipul Gupta, Kush R. Varshney, S. Shyam Sundar, Amulya Yadav:
Hey GPT, Can You be More Racist? Analysis from Crowdsourced Attempts to Elicit Biased Content from Generative AI. CoRR abs/2410.15467 (2024) - [i84]Vaishak Belle, Hana Chockler, Shannon Vallor, Kush R. Varshney, Joost Vennekens, Sander Beckers:
Trustworthiness and Responsibility in AI - Causality, Learning, and Verification (Dagstuhl Seminar 24121). Dagstuhl Reports 14(3): 75-91 (2024) - 2023
- [j45]Bran Knowles, Jason D'Cruz, John T. Richards, Kush R. Varshney:
Humble AI. Commun. ACM 66(9): 73-79 (2023) - [j44]Katy Ilonka Gero
The incentive gap in data work in the era of large models. Nat. Mac. Intell. 5(6): 565-567 (2023) - [j43]Girmaw Abebe Tadesse, Celia Cintas
Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning. npj Digit. Medicine 6 (2023) - [c97]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: 6788-6796 - [c96]Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying:
Minimax AUC Fairness: Efficient Algorithm with Provable Convergence. AAAI 2023: 11909-11917 - [c95]Bran Knowles
Trustworthy AI and the Logics of Intersectional Resistance. FAccT 2023: 172-182 - [c94]Brianna Richardson
Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness. FAccT 2023: 736-752 - [c93]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 - [c92]Kush R. Varshney, Lav R. Varshney:
A Banal Account of a Safety-Creativity Tradeoff in Generative AI 163-165. IUI Workshops 2023: 163-165 - [i83]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) - [i82]Yihua Zhang, Pranay Sharma
What Is Missing in IRM Training and Evaluation? Challenges and Solutions. CoRR abs/2303.02343 (2023) - [i81]Baihan Lin, Djallel Bouneffouf, Guillermo A. Cecchi, Kush R. Varshney:
Towards Healthy AI: Large Language Models Need Therapists Too. CoRR abs/2304.00416 (2023) - [i80]Ioana Baldini, Chhavi Yadav, Payel Das, Kush R. Varshney:
Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models. CoRR abs/2305.12620 (2023) - [i79]Kush R. Varshney:
Decolonial AI Alignment: Viśesadharma, Argument, and Artistic Expression. CoRR abs/2309.05030 (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
Human-centered explainability for life sciences, healthcare, and medical informatics. Patterns 3(5): 100493 (2022) - [c91]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 - [c90]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 - [c89]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 - [c88]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 - [c87]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 - [c86]Sainyam Galhotra, Karthikeyan Shanmugam
Causal Feature Selection for Algorithmic Fairness. SIGMOD Conference 2022: 276-285 - [c85]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
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) - [c84]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 - [c83]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 - [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
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 W. J. 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
AI Explainability 360 Toolkit. COMAD/CODS 2021: 376-379 - [c78]Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam
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
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. NeurIPS 2021: 21668-21680 - [c75]Tim Draws
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
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
Fair Enough: Improving Fairness in Budget-Constrained Decision Making Using Confidence Thresholds. SafeAI@AAAI 2020: 41-53 - [c73]Debarun Bhattacharjya, Karthikeyan Shanmugam
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
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
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
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
Preservation of Anomalous Subgroups On Variational Autoencoder Transformed Data. ICASSP 2020: 3627-3631 - [c62]Kartik Ahuja, Karthikeyan Shanmugam
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 M. Gruen, Kenney Ng
Tutorial on Human-Centered Explainability for Healthcare. KDD 2020: 3547-3548 - [c58]Newton M. Kinyanjui, Timothy Odonga, Celia Cintas
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
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
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
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
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
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
Think Your Artificial Intelligence Software Is Fair? Think Again. IEEE Softw. 36(4): 76-80 (2019) - [c57]Amanda Coston
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
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
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]Ravi Kiran Raman, Roman Vaculín, Michael Hind, Sekou L. Remy, Eleftheria Kyriaki Pissadaki, Nelson Kibichii Bore, Roozbeh Daneshvar, Biplav Srivastava, Kush R. Varshney:
Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach. CoRR abs/1809.08438 (2018) - [i29]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) - [i28]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 via a Blockchain Network. CoRR abs/1810.11126 (2018) - [i27]Minh N. B. Nguyen, Samuel Thomas, Anne E. Gattiker, Sujatha Kashyap, Kush R. Varshney:
SimplerVoice: A Key Message & Visual Description Generator System for Illiteracy. CoRR abs/1811.01299 (2018) - [i26]Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic:
TED: Teaching AI to Explain its Decisions. CoRR abs/1811.04896 (2018) - [i25]Vidya Muthukumar, Tejaswini Pedapati, Nalini K. Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney:
Understanding Unequal Gender Classification Accuracy from Face Images. CoRR abs/1812.00099 (2018) - [i24]Pranay Kr. Lohia, Karthikeyan Natesan Ramamurthy, Manish Bhide, Diptikalyan Saha, Kush R. Varshney, Ruchir Puri:
Bias Mitigation Post-processing for Individual and Group Fairness. CoRR abs/1812.06135 (2018) - 2017
- [j26]Kush R. Varshney, Homa Alemzadeh:
On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products. Big Data 5(3): 246-255 (2017) - [j25]Aleksandra Mojsilovic, Kush R. Varshney:
Preface: Data Science for Social Good. IBM J. Res. Dev. 61(6): 1-4 (2017) - [j24]Yumeng Tao, Debarun Bhattacharjya, Aliza R. Heching, Aditya Vempaty, Moninder Singh, Felix Lam
Effectiveness of peer detailing in a diarrhea program in Nigeria. IBM J. Res. Dev. 61(6): 1:1-1:12 (2017) - [j23]H. Lamba, M. E. Helander, Moninder Singh, N. Lethif, Achyutram Bhamidipaty, S. A. Baset, Aleksandra Mojsilovic, Kush R. Varshney:
Understanding the ecospace of philanthropic projects. IBM J. Res. Dev. 61(6): 6:1-6:10 (2017) - [j22]Kien Pham, Prasanna Sattigeri, Amit Dhurandhar, A. C. Jacob, M. Vukovic, P. Chataigner, Juliana Freire, Aleksandra Mojsilovic, Kush R. Varshney:
Real-time understanding of humanitarian crises via targeted information retrieval. IBM J. Res. Dev. 61(6): 7:1-7:12 (2017) - [j21]Evan Patterson
Dataflow representation of data analyses: Toward a platform for collaborative data science. IBM J. Res. Dev. 61(6): 9:1-9:13 (2017) - [j20]Caitlin Kuhlman, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Aurélie C. Lozano, Lei Cao
How to foster innovation: A data-driven approach to measuring economic competitiveness. IBM J. Res. Dev. 61(6): 11:1-11:12 (2017) - [j19]Lav R. Varshney
Decision Making With Quantized Priors Leads to Discrimination. Proc. IEEE 105(2): 241-255 (2017) - [j18]Kush R. Varshney:
Signal Processing for Social Good [In the Spotlight]. IEEE Signal Process. Mag. 34(3): 112-108 (2017) - [c45]Evan Patterson, Ioana Baldini, Aleksandra Mojsilovic, Kush R. Varshney:
Machine Representation of Data Analyses: Towards a Platform for Collaborative Data Science. AAAI Spring Symposia 2017 - [c44]Yumeng Tao, Debarun Bhattacharjya, Aliza R. Heching, Aditya Vempaty, Moninder Singh, Felix Lam, Kush R. Varshney, Aleksandra Mojsilovic:
Statistical Analysis of Peer Detailing for Children's Diarrhea Treatments. AAAI Spring Symposia 2017 - [c43]Jinfeng Yi, Cho-Jui Hsieh, Kush R. Varshney, Lijun Zhang, Yao Li:
Scalable Demand-Aware Recommendation. NIPS 2017: 2412-2421 - [c42]Flávio P. Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Optimized Pre-Processing for Discrimination Prevention. NIPS 2017: 3992-4001 - [c41]Alexandra Olteanu, Kartik Talamadupula, Kush R. Varshney:
The Limits of Abstract Evaluation Metrics: The Case of Hate Speech Detection. WebSci 2017: 405-406 - [i23]Jinfeng Yi, Cho-Jui Hsieh, Kush R. Varshney, Lijun Zhang, Yao Li:
Positive-Unlabeled Demand-Aware Recommendation. CoRR abs/1702.06347 (2017) - [i22]Flávio du Pin Calmon, Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Optimized Data Pre-Processing for Discrimination Prevention. CoRR abs/1704.03354 (2017) - [i21]Been Kim, Dmitry M. Malioutov, Kush R. Varshney, Adrian Weller:
Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017). CoRR abs/1708.02666 (2017) - [i20]Samiulla Shaikh, Harit Vishwakarma, Sameep Mehta, Kush R. Varshney, Karthikeyan Natesan Ramamurthy, Dennis Wei:
An End-To-End Machine Learning Pipeline That Ensures Fairness Policies. CoRR abs/1710.06876 (2017) - [i19]Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Distribution-Preserving k-Anonymity. CoRR abs/1711.01514 (2017) - [i18]Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, Justin Dauwels:
Neurology-as-a-Service for the Developing World. CoRR abs/1711.06195 (2017) - 2016
- [j17]Kush R. Varshney, Lav R. Varshney:
Olfactory signal processing. Digit. Signal Process. 48: 84-92 (2016) - [c40]Kush R. Varshney:
Interpretable machine learning via convex cardinal shape composition. Allerton 2016: 327-330 - [c39]Raya Horesh, Kush R. Varshney, Jinfeng Yi:
Information retrieval, fusion, completion, and clustering for employee expertise estimation. IEEE BigData 2016: 1385-1393 - [c38]Lav R. Varshney, Kush R. Varshney:
Fidelity loss in distribution-preserving anonymization and histogram equalization. CISS 2016: 24-29 - [c37]Aurélie C. Lozano, Prasanna Sattigeri, Aleksandra Mojsilovic, Kush R. Varshney:
Stable estimation of Granger-causal factors of country-level innovation. GlobalSIP 2016: 1290-1294 - [c36]Kush R. Varshney:
Engineering safety in machine learning. ITA 2016: 1-5 - [c35]Aleksandr Y. Aravkin, Kush R. Varshney, Liu Yang:
Dynamic matrix factorization with social influence. MLSP 2016: 1-6 - [c34]Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov:
Learning sparse two-level boolean rules. MLSP 2016: 1-6 - [i17]Kush R. Varshney:
Engineering Safety in Machine Learning. CoRR abs/1601.04126 (2016) - [i16]Aleksandr Y. Aravkin, Kush R. Varshney, Liu Yang:
Dynamic matrix factorization with social influence. CoRR abs/1604.06194 (2016) - [i15]Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov:
Interpretable Two-level Boolean Rule Learning for Classification. CoRR abs/1606.05798 (2016) - [i14]Prasanna Sattigeri, Aurélie C. Lozano, Aleksandra Mojsilovic, Kush R. Varshney, Mahmoud Naghshineh:
Understanding Innovation to Drive Sustainable Development. CoRR abs/1606.06177 (2016) - [i13]Kush R. Varshney:
Proceedings of the 2016 ICML Workshop on #Data4Good: Machine Learning in Social Good Applications. CoRR abs/1607.02450 (2016) - [i12]Been Kim, Dmitry M. Malioutov, Kush R. Varshney:
Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016). CoRR abs/1607.02531 (2016) - [i11]Kush R. Varshney, Homa Alemzadeh:
On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products. CoRR abs/1610.01256 (2016) - 2015
- [j16]Kush R. Varshney, George H. Chen, Brian Abelson, Kendall Nowocin, Vivek Sakhrani, Ling Xu, Brian L. Spatocco:
Targeting Villages for Rural Development Using Satellite Image Analysis. Big Data 3(1): 41-53 (2015) - [j15]Kush R. Varshney, Dennis Wei, Karthikeyan Natesan Ramamurthy, Aleksandra Mojsilovic:
Data Challenges in Disease Response: The 2014 Ebola Outbreak and Beyond. ACM J. Data Inf. Qual. 6(2-3): 5:1-5:3 (2015) - [c33]Dennis Wei, Kush R. Varshney, Marcy Wagman:
Optigrow: People Analytics for Job Transfers. BigData Congress 2015: 535-542 - [c32]Sanjeeb Dash, Dmitry M. Malioutov, Kush R. Varshney:
Learning interpretable classification rules using sequential rowsampling. ICASSP 2015: 3337-3341 - [c31]Dennis Wei, Kush R. Varshney:
Robust binary hypothesis testing under contaminated likelihoods. ICASSP 2015: 3407-3411 - [c30]Kush R. Varshney, Karthikeyan Natesan Ramamurthy:
Persistent topology of decision boundaries. ICASSP 2015: 3931-3935 - [c29]Aleksandra Mojsilovic, Kush R. Varshney:
Assessing Expertise in the Enterprise: The Recommender Point of View. RecSys 2015: 231 - [c28]Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Health Insurance Market Risk Assessment: Covariate Shift and k-Anonymity. SDM 2015: 226-234 - [i10]Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov:
Interpretable Two-level Boolean Rule Learning for Classification. CoRR abs/1511.07361 (2015) - 2014
- [j14]Kush R. Varshney:
Bounded Confidence Opinion Dynamics in a Social Network of Bayesian Decision Makers. IEEE J. Sel. Top. Signal Process. 8(4): 576-585 (2014) - [j13]Müjdat Çetin
Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing. IEEE Signal Process. Mag. 31(4): 27-40 (2014) - [j12]Kush R. Varshney, Lav R. Varshney:
Optimal Grouping for Group Minimax Hypothesis Testing. IEEE Trans. Inf. Theory 60(10): 6511-6521 (2014) - [j11]John Z. Sun, Dhruv Parthasarathy, Kush R. Varshney:
Collaborative Kalman Filtering for Dynamic Matrix Factorization. IEEE Trans. Signal Process. 62(14): 3499-3509 (2014) - [c27]Sanjeeb Dash, Dmitry M. Malioutov, Kush R. Varshney:
Screening for learning classification rules via Boolean compressed sensing. ICASSP 2014: 3360-3364 - [c26]Brian Abelson, Kush R. Varshney, Joy Sun:
Targeting direct cash transfers to the extremely poor. KDD 2014: 1563-1572 - [c25]Kush R. Varshney, Vijil Chenthamarakshan, Scott W. Fancher, Jun Wang, DongPing Fang, Aleksandra Mojsilovic:
Predicting employee expertise for talent management in the enterprise. KDD 2014: 1729-1738 - [c24]Kush R. Varshney, Lav R. Varshney:
FOOD steganography with olfactory white. SSP 2014: 21-24 - [c23]Kush R. Varshney, Lav R. Varshney:
Active odor cancellation. SSP 2014: 25-28 - [c22]Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Jayaraman J. Thiagarajan:
Computing persistent homology under random projection. SSP 2014: 105-108 - [r1]Jun Wang, Kush R. Varshney, Aleksandra Mojsilovic:
Legislative Prediction with Political and Social Network Analysis. Encyclopedia of Social Network Analysis and Mining 2014: 804-811 - [i9]Dennis Wei, Kush R. Varshney:
Robust Binary Hypothesis Testing Under Contaminated Likelihoods. CoRR abs/1410.0952 (2014) - [i8]Kush R. Varshney, Lav R. Varshney:
Olfactory Signals and Systems. CoRR abs/1410.4865 (2014) - 2013
- [j10]Kush R. Varshney, Ryan J. Prenger, Tracy L. Marlatt, Barry Y. Chen, William G. Hanley:
Practical Ensemble Classification Error Bounds for Different Operating Points. IEEE Trans. Knowl. Data Eng. 25(11): 2590-2601 (2013) - [c21]Lav R. Varshney, Florian Pinel, Kush R. Varshney, Angela Schörgendorfer, Yi-Min Chee:
Cognition as a part of computational creativity. ICCI*CC 2013: 36-43 - [c20]Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Moninder Singh:
Quantile regression for workforce analytics. GlobalSIP 2013: 1134 - [c19]Kush R. Varshney:
Opinion dynamics with bounded confidence in the Bayes risk error divergence sense. ICASSP 2013: 6600-6604 - [c18]Ban Kawas, Mark S. Squillante, Dharmashankar Subramanian, Kush R. Varshney:
Prescriptive Analytics for Allocating Sales Teams to Opportunities. ICDM Workshops 2013: 211-218 - [c17]DongPing Fang, Kush R. Varshney, Jun Wang, Karthikeyan Natesan Ramamurthy, Aleksandra Mojsilovic, John H. Bauer:
Quantifying and Recommending Expertise When New Skills Emerge. ICDM Workshops 2013: 672-679 - [c16]Dmitry M. Malioutov, Kush R. Varshney:
Exact Rule Learning via Boolean Compressed Sensing. ICML (3) 2013: 765-773 - [c15]Kush R. Varshney, Peter M. van de Ven:
Balancing lifetime and classification accuracy of wireless sensor networks. MobiHoc 2013: 31-38 - [i7]Kush R. Varshney, Lav R. Varshney:
Optimal Grouping for Group Minimax Hypothesis Testing. CoRR abs/1307.6512 (2013) - [i6]Kush R. Varshney, Lav R. Varshney, Jun Wang, Daniel Myers:
Flavor Pairing in Medieval European Cuisine: A Study in Cooking with Dirty Data. CoRR abs/1307.7982 (2013) - [i5]Kush R. Varshney:
Bounded Confidence Opinion Dynamics in a Social Network of Bayesian Decision Makers. CoRR abs/1309.3959 (2013) - [i4]Lav R. Varshney, Florian Pinel, Kush R. Varshney, Debarun Bhattacharjya, Angela Schörgendorfer, Yi-Min Chee:
A Big Data Approach to Computational Creativity. CoRR abs/1311.1213 (2013) - 2012
- [j9]Moritz Baier, Jorge E. Carballo, Alice J. Chang, Yingdong Lu, Aleksandra Mojsilovic, M. Jonathan Richard, Moninder Singh, Mark S. Squillante, Kush R. Varshney:
Sales-force performance analytics and optimization. IBM J. Res. Dev. 56(6): 8 (2012) - [j8]Kush R. Varshney:
Generalization Error of Linear Discriminant Analysis in Spatially-Correlated Sensor Networks. IEEE Trans. Signal Process. 60(6): 3295-3301 (2012) - [c14]John Z. Sun, Kush R. Varshney, Karthik Subbian:
Dynamic matrix factorization: A state space approach. ICASSP 2012: 1897-1900 - [c13]Moninder Singh, Kush R. Varshney, Jun Wang, Aleksandra Mojsilovic, Alisia R. Gill, Patricia I. Faur, Raphael Ezry:
An Analytics Approach for Proactively Combating Voluntary Attrition of Employees. ICDM Workshops 2012: 317-323 - [c12]Kush R. Varshney, Jamie C. Rasmussen, Aleksandra Mojsilovic, Moninder Singh, Joan Morris DiMicco:
Interactive Visual Salesforce Analytics. ICIS 2012 - [c11]Jun Wang, Kush R. Varshney, Aleksandra Mojsilovic:
Legislative Prediction via Random Walks over a Heterogeneous Graph. SDM 2012: 1095-1106 - [c10]Jun Wang, Moninder Singh, Kush R. Varshney:
Does selection bias blind performance diagnostics of business decision models? A case study in salesforce optimization. SOLI 2012: 416-421 - [c9]Kush R. Varshney, Moninder Singh, Jun Wang:
Decision trees for heterogeneous dose-response signal analysis. SSP 2012: 904-907 - [i3]Kush R. Varshney, Peter M. van de Ven:
Balancing Lifetime and Classification Accuracy of Wireless Sensor Networks. CoRR abs/1208.2278 (2012) - 2011
- [j7]Kush R. Varshney, Aleksandra Mojsilovic:
Business Analytics Based on Financial Time Series. IEEE Signal Process. Mag. 28(5): 83-93 (2011) - [j6]Kush R. Varshney, Alan S. Willsky:
Linear Dimensionality Reduction for Margin-Based Classification: High-Dimensional Data and Sensor Networks. IEEE Trans. Signal Process. 59(6): 2496-2512 (2011) - [j5]Kush R. Varshney:
Bayes Risk Error is a Bregman Divergence. IEEE Trans. Signal Process. 59(9): 4470-4472 (2011) - [c8]Kush R. Varshney:
Spatially-correlated sensor discriminant analysis. ICASSP 2011: 3680-3683 - [c7]Dmitriy A. Katz-Rogozhnikov, Kush R. Varshney, Aleksandra Mojsilovic, Moninder Singh:
MCMC inference of the shape and variability of time-response signals. ICASSP 2011: 3956-3959 - [c6]Kush R. Varshney, Moninder Singh, Mayank Sharma, Aleksandra Mojsilovic:
Estimating Post-Event Seller Productivity Profiles in Dynamic Sales Organizations. ICDM Workshops 2011: 1191-1198 - [c5]Lav R. Varshney, Joong Bum Rhim, Kush R. Varshney, Vivek K. Goyal:
Categorical decision making by people, committees, and crowds. ITA 2011: 425-434 - [i2]John Z. Sun, Kush R. Varshney, Karthik Subbian:
Dynamic Matrix Factorization: A State Space Approach. CoRR abs/1110.2098 (2011) - 2010
- [b1]Kush R. Varshney:
Frugal hypothesis testing and classification. Massachusetts Institute of Technology, Cambridge, MA, USA, 2010 - [j4]Kush R. Varshney, Alan S. Willsky:
Classification Using Geometric Level Sets. J. Mach. Learn. Res. 11: 491-516 (2010) - [c4]Ryan J. Prenger, Tracy D. Lemmond, Kush R. Varshney, Barry Y. Chen, William G. Hanley:
Class-specific error bounds for ensemble classifiers. KDD 2010: 843-852
2000 – 2009
- 2009
- [j3]Kush R. Varshney, Nikos Paragios, Jean-François Deux, Alain Kulski, Rémy Raymond, Phillipe Hernigou, Alain Rahmouni:
Postarthroplasty Examination Using X-Ray Images. IEEE Trans. Medical Imaging 28(3): 469-474 (2009) - [c3]Kush R. Varshney, Alan S. Willsky:
Learning dimensionality-reduced classifiers for information fusion. FUSION 2009: 1881-1888 - 2008
- [j2]Kush R. Varshney, Müjdat Çetin
Sparse Representation in Structured Dictionaries With Application to Synthetic Aperture Radar. IEEE Trans. Signal Process. 56(8-1): 3548-3561 (2008) - [j1]Kush R. Varshney, Lav R. Varshney:
Quantization of Prior Probabilities for Hypothesis Testing. IEEE Trans. Signal Process. 56(10-1): 4553-4562 (2008) - [c2]Kush R. Varshney, Lav R. Varshney:
Minimum mean bayes risk error quantization of prior probabilities. ICASSP 2008: 3445-3448 - [i1]Kush R. Varshney, Lav R. Varshney:
Quantization of Prior Probabilities for Hypothesis Testing. CoRR abs/0805.4338 (2008) - 2007
- [c1]Kush R. Varshney, Nikos Paragios, Alain Kulski, Rémy Raymond, Phillipe Hernigou, Alain Rahmouni:
Multi-View Stereo Reconstruction of Total Knee Replacement from X-Rays. ISBI 2007: 1148-1151
Coauthor Index
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