Moninder Singh

Publication Activity (10 Years)

Years Active: 1993-2024
Publications (10 Years): 44

Top Topics

Reinforcement Learning

Machine Learning

Top Venues

IBM J. Res. Dev.

CHI Extended Abstracts

Publications

Rosario Uceda-Sosa, Karthikeyan Natesan Ramamurthy, Maria Chang, Moninder Singh
Reasoning about concepts with LLMs: Inconsistencies abound. CoRR (2024)
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 (2024)
Amit Dhurandhar, Rahul Nair, Moninder Singh, Elizabeth Daly, Karthikeyan Natesan Ramamurthy
Ranking Large Language Models without Ground Truth. ACL (Findings) (2024)
Amit Dhurandhar, Rahul Nair, Moninder Singh, Elizabeth Daly, Karthikeyan Natesan Ramamurthy
Ranking Large Language Models without Ground Truth. CoRR (2024)
Manish Nagireddy, Lamogha Chiazor, Moninder Singh, Ioana Baldini
SocialStigmaQA: A Benchmark to Uncover Stigma Amplification in Generative Language Models. AAAI (2024)
Manish Nagireddy, Lamogha Chiazor, Moninder Singh, Ioana Baldini
SocialStigmaQA: A Benchmark to Uncover Stigma Amplification in Generative Language Models. CoRR (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)
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)
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)
Hammaad Adam, Ming-Ying Yang, Kenrick Cato, Ioana Baldini, Charles Senteio, Leo Anthony Celi, Jiaming Zeng, Moninder Singh, Marzyeh Ghassemi
Write It Like You See It: Detectable Differences in Clinical Notes by Race Lead to Differential Model Recommendations. AIES (2022)
Tsuyoshi Idé, Amit Dhurandhar, Jirí Navrátil, Moninder Singh, Naoki Abe
Anomaly Attribution with Likelihood Compensation. CoRR (2022)
Hammaad Adam, Ming-Ying Yang, Kenrick Cato, Ioana Baldini, Charles Senteio, Leo Anthony Celi, Jiaming Zeng, Moninder Singh, Marzyeh Ghassemi
Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations. 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)
Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, Moninder Singh, Mikhail Yurochkin
Your fairness may vary: Pretrained language model fairness in toxic text classification. ACL (Findings) (2022)
Kofi Arhin, Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, Moninder Singh
Ground-Truth, Whose Truth? - Examining the Challenges with Annotating Toxic Text Datasets. 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)
Q. Vera Liao, Moninder Singh, Yunfeng Zhang, Rachel K. E. Bellamy
Introduction to Explainable AI. CHI Extended Abstracts (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)
Yunfeng Zhang, Rachel K. E. Bellamy, Q. Vera Liao, Moninder Singh
Introduction to AI Fairness. CHI Extended Abstracts (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)
Tsuyoshi Idé, Amit Dhurandhar, Jirí Navrátil, Moninder Singh, Naoki Abe
Anomaly Attribution with Likelihood Compensation. AAAI (2021)
Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, Mikhail Yurochkin, Moninder Singh
Your fairness may vary: Group fairness of pretrained language models in toxic text classification. 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: An Extensible Toolkit for Understanding Data and Machine Learning Models. J. Mach. Learn. Res. 21 (2020)
Yunfeng Zhang, Rachel K. E. Bellamy, Moninder Singh, Q. Vera Liao
Introduction to AI Fairness. CHI Extended Abstracts (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)
Q. Vera Liao, Moninder Singh, Yunfeng Zhang, Rachel K. E. Bellamy
Introduction to Explainable AI. CHI Extended Abstracts (2020)
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)
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)
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) (2019)
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 (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. IBM J. Res. Dev. 63 (4/5) (2019)
Moninder Singh, Karthikeyan Natesan Ramamurthy
Understanding racial bias in health using the Medical Expenditure Panel Survey data. CoRR (2019)
Shrihari Vasudevan, Moninder Singh, Joydeep Mondal, Michael Peran, Benjamin Zweig, Brian Johnston, Rachel Rosenfeld
Estimating Fungibility Between Skills by Combining Skill Similarities Obtained from Multiple Data Sources. Data Sci. Eng. 3 (3) (2018)
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 (2018)
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)
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 (2018)
Yumeng Tao, Debarun Bhattacharjya, Aliza R. Heching, Aditya Vempaty, Moninder Singh, Felix Lam, Jan Houdek, M. Abubakar, Abdulwahab Alharbi, T. Braimoh, N. Ihebuzor, Aleksandra Mojsilovic, Kush R. Varshney
Effectiveness of peer detailing in a diarrhea program in Nigeria. IBM J. Res. Dev. 61 (6) (2017)
Shrihari Vasudevan, Moninder Singh, Joydeep Mondal, Michael Peran, Ben Zweig, Brian Johnston, Rachel Rosenfeld
Estimating Fungibility Between Skills by Combining Skill-Similarities Obtained from Multiple Data Sources. ICDM Workshops (2017)
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) (2017)
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)
Karthikeyan Natesan Ramamurthy, Dennis Wei, Emily Ray, Moninder Singh, Vijay S. Iyengar, Dmitriy A. Katz-Rogozhnikov, Jingwei Yang, Kevin N. Tran, Gigi Y. Yuen-Reed
A configurable, big data system for on-demand healthcare cost prediction. IEEE BigData (2017)
Moninder Singh, Karthikeyan Natesan Ramamurthy, Shrihari Vasudevan
Propensity modeling for employee Re-skilling. GlobalSIP (2017)
Karthikeyan Natesan Ramamurthy, Moninder Singh, Yichong Yu, Jessica Aspis, Matthew Iames, Michael Peran, Qin S. Held
A talent management tool using propensity to leave analytics. DSAA (2015)
Karthikeyan Natesan Ramamurthy, Moninder Singh, Michael Davis, J. Alex Kevern, Uri Klein, Michael Peran
Identifying Employees for Re-skilling Using an Analytics-Based Approach. ICDM Workshops (2015)
Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Moninder Singh
Quantile regression for workforce analytics. GlobalSIP (2013)
Moninder Singh, Marco Valtorta
An Algorithm for the Construction of Bayesian Network Structures from Data CoRR (2013)
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)
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) (2012)
Kush R. Varshney, Jamie C. Rasmussen, Aleksandra Mojsilovic, Moninder Singh, Joan Morris DiMicco
Interactive Visual Salesforce Analytics. ICIS (2012)
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)
Kush R. Varshney, Moninder Singh, Jun Wang
Decision trees for heterogeneous dose-response signal analysis. SSP (2012)
Dmitriy A. Katz-Rogozhnikov, Kush R. Varshney, Aleksandra Mojsilovic, Moninder Singh
MCMC inference of the shape and variability of time-response signals. ICASSP (2011)
Kush R. Varshney, Moninder Singh, Mayank Sharma, Aleksandra Mojsilovic
Estimating Post-Event Seller Productivity Profiles in Dynamic Sales Organizations. ICDM Workshops (2011)
Alexandru Niculescu-Mizil, Claudia Perlich, Grzegorz Swirszcz, Vikas Sindhwani, Yan Liu, Prem Melville, Dong Wang, Jing Xiao, Jianying Hu, Moninder Singh, Wei Xiong Shang, Yanfeng Zhu
Winning the KDD Cup Orange Challenge with Ensemble Selection. KDD Cup (2009)
Afsaneh Shirazi, Jianying Hu, Moninder Singh, Mark S. Squillante, Aleksandra Mojsilovic
A framework for combined bayesian analysis and optimization for services delivery. SOLI (2009)
Hisashi Kashima, Jianying Hu, Bonnie K. Ray, Moninder Singh
K-means clustering of proportional data using L1 distance. ICPR (2008)
Jianying Hu, Moninder Singh, Aleksandra Mojsilovic
Categorization using semi-supervised clustering. ICPR (2008)
Jianying Hu, Bonnie K. Ray, Moninder Singh
Statistical methods for automated generation of service engagement staffing plans. IBM J. Res. Dev. 51 (3/4) (2007)
Moninder Singh, Jayant Kalagnanam, Sudhir Verma, Amit J. Shah, Swaroop K. Chalasani
Automated cleansing for spend analytics. CIKM (2005)
Sastry Duri, Jeffrey Elliott, Marco Gruteser, Xuan Liu, Paul Moskowitz, Ronald Perez, Moninder Singh, Jung-Mu Tang
Data Protection and Data Sharing in Telematics. Mob. Networks Appl. 9 (6) (2004)
Jayant Kalagnanam, Moninder Singh, Sudhir Verma, Michael Patek, Yuk Wah Wong
A system for automated mapping of bill-of-materials part numbers. KDD (2004)
Kathy Bohrer, Xuan Liu, Sean McLaughlin, Edith Schonberg, Moninder Singh
Object Oriented XML Query by Example. ER (Workshops) (2003)
Sastry Duri, Marco Gruteser, Xuan Liu, Paul Moskowitz, Ronald Perez, Moninder Singh, Jung-Mu Tang
Framework for security and privacy in automotive telematics. Workshop Mobile Commerce (2002)
John McCarthy, Marvin Minsky, Aaron Sloman, Leiguang Gong, Tessa A. Lau, Leora Morgenstern, Erik T. Mueller, Doug Riecken, Moninder Singh, Push Singh
An architecture of diversity for commonsense reasoning. IBM Syst. J. 41 (3) (2002)
Mark K. Singley, Moninder Singh, Peter G. Fairweather, Robert G. Farrell, Steven Swerling
Algebra jam: supporting teamwork and managing roles in a collaborative learning environment. CSCW (2000)
Leora Morgenstern, Moninder Singh
An Expert System Using Nonmonotonic Techniques for Beneefits Inquiry in the Insurance Industry. IJCAI (1) (1997)
Moninder Singh
Learning Bayesian Networks from Incomplete Data. AAAI/IAAI (1997)
Moninder Singh
Dynamic Constraint-Based Planning in Trauma Management. AAAI/IAAI, Vol. 2 (1996)
Moninder Singh
Induction of Selective Bayesian Networks from Data. AAAI/IAAI, Vol. 2 (1996)
Moninder Singh
Path Consistency Revisited. Int. J. Artif. Intell. Tools 5 (1-2) (1996)
Moninder Singh, Gregory M. Provan
Efficient Learning of Selective Bayesian Network Classifiers. ICML (1996)
Gregory M. Provan, Moninder Singh
Data Mining and Model Simplicity: A Case Study in Diagnosis. KDD (1996)
Kazuo J. Ezawa, Moninder Singh, Steven W. Norton
Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management. ICML (1996)
Gregory M. Provan, Moninder Singh
Learning Bayesian Networks Using Feature Selection. AISTATS (1995)
Moninder Singh, Marco Valtorta
Construction of Bayesian network structures from data: A brief survey and an efficient algorithm. Int. J. Approx. Reason. 12 (2) (1995)
Moninder Singh, Gregory M. Provan
A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers. ICML (1995)
Moninder Singh
Path consistency revisited. ICTAI (1995)
Moninder Singh, Marco Valtorta
An Algorithm for the Construction of Bayesian Network Structures from Data. UAI (1993)