Vision Paper: Enabling Privacy for the Paranoids. - Gagan Aggarwal; Mayank Bawa; Prasanna Ganesan; Hector Garcia-Molina; Krishnaram Kenthapadi; Nina Mishra; Rajeev Motwani 001; Utkarsh Srivastava; Dilys Thomas; Jennifer Widom; Ying Xu 002 (VLDB, 2004)

Two Can Keep A Secret: A Distributed Architecture for Secure Database Services. - Gagan Aggarwal; Mayank Bawa; Prasanna Ganesan; Hector Garcia-Molina; Krishnaram Kenthapadi; Rajeev Motwani 001; Utkarsh Srivastava; Dilys Thomas; Ying Xu 002 (CIDR, 2005)

Anonymizing Tables. - Gagan Aggarwal; Tomás Feder; Krishnaram Kenthapadi; Rajeev Motwani 001; Rina Panigrahy; Dilys Thomas; An Zhu (ICDT, 2005)

Simulatable auditing. - Krishnaram Kenthapadi; Nina Mishra; Kobbi Nissim (PODS, 2005)

Decentralized algorithms using both local and random probes for P2P load balancing. - Krishnaram Kenthapadi; Gurmeet Singh Manku (SPAA, 2005)

Our Data, Ourselves: Privacy Via Distributed Noise Generation. - Cynthia Dwork; Krishnaram Kenthapadi; Frank McSherry; Ilya Mironov; Moni Naor (EUROCRYPT, 2006)

Balanced allocation on graphs. - Krishnaram Kenthapadi; Rina Panigrahy (SODA, 2006)

Towards Robustness in Query Auditing. - Shubha U. Nabar; Bhaskara Marthi; Krishnaram Kenthapadi; Nina Mishra; Rajeev Motwani 001 (VLDB, 2006)

Models and algorithms for data privacy. - Krishnaram Kenthapadi (2006)

A Survey of Query Auditing Techniques for Data Privacy. - Shubha U. Nabar; Krishnaram Kenthapadi; Nina Mishra; Rajeev Motwani 001 (Privacy-Preserving Data Mining, 2008)

Generating labels from clicks. - Rakesh Agrawal 001; Alan Halverson; Krishnaram Kenthapadi; Nina Mishra; Panayiotis Tsaparas (WSDM, 2009)

Releasing search queries and clicks privately. - Aleksandra Korolova; Krishnaram Kenthapadi; Nina Mishra; Alexandros Ntoulas (WWW, 2009)

Enriching textbooks through data mining. - Rakesh Agrawal 001; Sreenivas Gollapudi; Krishnaram Kenthapadi; Nitish Srivastava; Raja Velu (ACM DEV, 2010)

Achieving anonymity via clustering. - Gagan Aggarwal; Rina Panigrahy; Tomás Feder; Dilys Thomas; Krishnaram Kenthapadi; Samir Khuller; An Zhu (ACM Trans. Algorithms, 2010)

Enriching textbooks with images. - Rakesh Agrawal 001; Sreenivas Gollapudi; Anitha Kannan; Krishnaram Kenthapadi (CIKM, 2011)

Enriching Education through Data Mining. - Rakesh Agrawal 001; Sreenivas Gollapudi; Anitha Kannan; Krishnaram Kenthapadi (PReMI, 2011)

Indexing strategies for graceful degradation of search quality. - Shuai Ding 006; Sreenivas Gollapudi; Samuel Ieong; Krishnaram Kenthapadi; Alexandros Ntoulas (SIGIR, 2011)

Identifying enrichment candidates in textbooks. - Rakesh Agrawal 001; Sreenivas Gollapudi; Anitha Kannan; Krishnaram Kenthapadi (WWW (Companion Volume), 2011)

Data mining for improving textbooks. - Rakesh Agrawal 001; Sreenivas Gollapudi; Anitha Kannan; Krishnaram Kenthapadi (SIGKDD Explor., 2011)

Quality of textbooks: an empirical study. - Rakesh Agrawal 001; Sunandan Chakraborty; Sreenivas Gollapudi; Anitha Kannan; Krishnaram Kenthapadi (ACM DEV, 2012)

Empowering authors to diagnose comprehension burden in textbooks. - Rakesh Agrawal 001; Sunandan Chakraborty; Sreenivas Gollapudi; Anitha Kannan; Krishnaram Kenthapadi (KDD, 2012)

Electronic Textbooks and Data Mining. - Rakesh Agrawal 001; Sreenivas Gollapudi; Anitha Kannan; Krishnaram Kenthapadi (WAIM, 2012)

Of hammers and nails: an empirical comparison of three paradigms for processing large graphs. - Marc Najork; Dennis Fetterly; Alan Halverson; Krishnaram Kenthapadi; Sreenivas Gollapudi (WSDM, 2012)

Studying from electronic textbooks. - Rakesh Agrawal 001; Sreenivas Gollapudi; Anitha Kannan; Krishnaram Kenthapadi (CIKM, 2013)

Group chats on Twitter. - James Cook; Krishnaram Kenthapadi; Nina Mishra (WWW, 2013)

Denials leak information: Simulatable auditing. - Krishnaram Kenthapadi; Nina Mishra; Kobbi Nissim (J. Comput. Syst. Sci., 2013)

Privacy via the Johnson-Lindenstrauss Transform. - Krishnaram Kenthapadi; Aleksandra Korolova; Ilya Mironov; Nina Mishra (J. Priv. Confidentiality, 2013)

Similarity Search using Concept Graphs. - Rakesh Agrawal 001; Sreenivas Gollapudi; Anitha Kannan; Krishnaram Kenthapadi (CIKM, 2014)

Ranking Twitter discussion groups. - James Cook; Abhimanyu Das; Krishnaram Kenthapadi; Nina Mishra (COSN, 2014)

Assigning Educational Videos at Appropriate Locations in Textbooks. - Marios Kokkodis; Anitha Kannan; Krishnaram Kenthapadi (EDM, 2014)

Mining Videos from the Web for Electronic Textbooks. - Rakesh Agrawal 001; Maria Christoforaki; Sreenivas Gollapudi; Anitha Kannan; Krishnaram Kenthapadi; Adith Swaminathan (ICFCA, 2014)

Evaluating educational interventions at scale. - Rakesh Agrawal 001; M. Hanif Jhaveri; Krishnaram Kenthapadi (L@S, 2014)

Assigning videos to textbooks at appropriate granularity. - Marios Kokkodis; Anitha Kannan; Krishnaram Kenthapadi (L@S, 2014)

CaSMoS: A Framework for Learning Candidate Selection Models over Structured Queries and Documents. - Fedor Borisyuk; Krishnaram Kenthapadi; David Stein 002; Bo Zhao 001 (KDD, 2016)

Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary. - Krishnaram Kenthapadi; Stuart Ambler; Liang Zhang 021; Deepak Agarwal (CIKM, 2017)

LiJAR: A System for Job Application Redistribution towards Efficient Career Marketplace. - Fedor Borisyuk; Liang Zhang 021; Krishnaram Kenthapadi (KDD, 2017)

LinkedIn Salary: A System for Secure Collection and Presentation of Structured Compensation Insights to Job Seekers. - Krishnaram Kenthapadi; Ahsan Chudhary; Stuart Ambler (PAC, 2017)

Personalized Job Recommendation System at LinkedIn: Practical Challenges and Lessons Learned. - Krishnaram Kenthapadi; Benjamin Le; Ganesh Venkataraman (RecSys, 2017)

Candidate Selection for Large Scale Personalized Search and Recommender Systems. - Dhruv Arya; Ganesh Venkataraman; Aman Grover; Krishnaram Kenthapadi (SIGIR, 2017)

Dionysius: A Framework for Modeling Hierarchical User Interactions in Recommender Systems. - Jian Wang; Krishnaram Kenthapadi; Kaushik Rangadurai; David Hardtke (CoRR, 2017)

PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn. - Krishnaram Kenthapadi; Thanh T. L. Tran (CIKM, 2018)

Towards Deep and Representation Learning for Talent Search at LinkedIn. - Rohan Ramanath; Hakan Inan; Gungor Polatkan; Bo Hu; Qi Guo 003; Cagri Ozcaglar; Xianren Wu; Krishnaram Kenthapadi; Sahin Cem Geyik (CIKM, 2018)

How LinkedIn Economic Graph Bonds Information and Product: Applications in LinkedIn Salary. - Xi Chen 049; Yiqun Liu 010; Liang Zhang 021; Krishnaram Kenthapadi (KDD, 2018)

Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned. - Sahin Cem Geyik; Qi Guo 003; Bo Hu; Cagri Ozcaglar; Ketan Thakkar; Xianren Wu; Krishnaram Kenthapadi (SIGIR, 2018)

Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. - Maria De-Arteaga; Alexey Romanov; Hanna M. Wallach; Jennifer T. Chayes; Christian Borgs; Alexandra Chouldechova; Sahin Cem Geyik; Krishnaram Kenthapadi; Adam Tauman Kalai (FAT, 2019)

Explainable AI in Industry. - Krishna Gade; Sahin Cem Geyik; Krishnaram Kenthapadi; Varun Mithal; Ankur Taly (KDD, 2019)

Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search. - Sahin Cem Geyik; Stuart Ambler; Krishnaram Kenthapadi (KDD, 2019)

What's in a Name? Reducing Bias in Bios without Access to Protected Attributes. - Alexey Romanov; Maria De-Arteaga; Hanna M. Wallach; Jennifer T. Chayes; Christian Borgs; Alexandra Chouldechova; Sahin Cem Geyik; Krishnaram Kenthapadi; Anna Rumshisky; Adam Kalai (NAACL-HLT (1), 2019)

Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned. - Sarah Bird; Krishnaram Kenthapadi; Emre Kiciman; Margaret Mitchell (WSDM, 2019)

Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned. - Sarah Bird; Ben Hutchinson; Krishnaram Kenthapadi; Emre Kiciman; Margaret Mitchell (WWW (Companion Volume), 2019)

Privacy-preserving Data Mining in Industry. - Krishnaram Kenthapadi; Ilya Mironov; Abhradeep Thakurta (WWW (Companion Volume), 2019)

LiFT: A Scalable Framework for Measuring Fairness in ML Applications. - Sriram Vasudevan; Krishnaram Kenthapadi (CIKM, 2020)

Bridging the gap from AI ethics research to practice. - Kathy Baxter; Yoav Schlesinger; Sarah Aerni; Lewis J. Baker; Julie Dawson; Krishnaram Kenthapadi; Isabel M. Kloumann; Hanna M. Wallach (FAT*, 2020)

Explainable AI in industry: practical challenges and lessons learned: implications tutorial. - Krishna Gade; Sahin Cem Geyik; Krishnaram Kenthapadi; Varun Mithal; Ankur Taly (FAT*, 2020)

Evaluating Fairness Using Permutation Tests. - Cyrus DiCiccio; Sriram Vasudevan; Kinjal Basu 001; Krishnaram Kenthapadi; Deepak Agarwal (KDD, 2020)

Explainable AI in Industry: Practical Challenges and Lessons Learned. - Krishna Gade; Sahin Cem Geyik; Krishnaram Kenthapadi; Varun Mithal; Ankur Taly (WWW (Companion Volume), 2020)

Fair Bayesian Optimization. - Valerio Perrone; Michele Donini; Krishnaram Kenthapadi; Cédric Archambeau (CoRR, 2020)

Fairness-Aware Online Personalization. - G. Roshan Lal; Sahin Cem Geyik; Krishnaram Kenthapadi (CoRR, 2020)

Convergent Algorithms for (Relaxed) Minimax Fairness. - Emily Diana; Wesley Gill; Michael Kearns; Krishnaram Kenthapadi; Aaron Roth 001 (CoRR, 2020)

Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. - Valerio Perrone; Huibin Shen; Aida Zolic; Iaroslav Shcherbatyi; Amr Ahmed 004; Tanya Bansal; Michele Donini; Fela Winkelmolen; Rodolphe Jenatton; Jean Baptiste Faddoul; Barbara Pogorzelska; Miroslav Miladinovic; et al. (CoRR, 2020)

On the Lack of Robust Interpretability of Neural Text Classifiers. - Muhammad Bilal Zafar; Michele Donini; Dylan Slack; Cédric Archambeau; Sanjiv Das; Krishnaram Kenthapadi (ACL/IJCNLP (Findings), 2021)

Minimax Group Fairness: Algorithms and Experiments. - Emily Diana; Wesley Gill; Michael Kearns; Krishnaram Kenthapadi; Aaron Roth 001 (AIES, 2021)

Towards Unbiased and Accurate Deferral to Multiple Experts. - Vijay Keswani; Matthew Lease; Krishnaram Kenthapadi (AIES, 2021)

Fair Bayesian Optimization. - Valerio Perrone; Michele Donini; Muhammad Bilal Zafar; Robin Schmucker; Krishnaram Kenthapadi; Cédric Archambeau (AIES, 2021)

Differentially Private Query Release Through Adaptive Projection. - Sergül Aydöre; William Brown; Michael Kearns; Krishnaram Kenthapadi; Luca Melis; Aaron Roth 001; Amaresh Ankit Siva (ICML, 2021)

Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud. - Michaela Hardt; Xiaoguang Chen; Xiaoyi Cheng; Michele Donini; Jason Gelman; Satish Gollaprolu; John He; Pedro Larroy; Xinyu Liu; Nick McCarthy; Ashish Rathi; Scott Rees; et al. (KDD, 2021)

Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. - Valerio Perrone; Huibin Shen; Aida Zolic; Iaroslav Shcherbatyi; Amr Ahmed 004; Tanya Bansal; Michele Donini; Fela Winkelmolen; Rodolphe Jenatton; Jean Baptiste Faddoul; Barbara Pogorzelska; Miroslav Miladinovic; et al. (KDD, 2021)

Amazon SageMaker Debugger: A System for Real-Time Insights into Machine Learning Model Training. - Nathalie Rauschmayr; Vikas Kumar; Rahul Huilgol; Andrea Olgiati; Satadal Bhattacharjee; Nihal Harish; Vandana Kannan; Amol Lele; Anirudh Acharya; Jared Nielsen; Lakshmi Ramakrishnan; Ishan Bhatt; et al. (MLSys, 2021)

Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy. - Dylan Slack; Nathalie Rauschmayr; Krishnaram Kenthapadi (CoRR, 2021)

On Measuring the Diversity of Organizational Networks. - Zeinab S. Jalali; Krishnaram Kenthapadi; Sucheta Soundarajan (CoRR, 2021)

More Than Words: Towards Better Quality Interpretations of Text Classifiers. - Muhammad Bilal Zafar; Philipp Schmidt 002; Michele Donini; Cédric Archambeau; Felix Biessmann; Sanjiv Ranjan Das; Krishnaram Kenthapadi (CoRR, 2021)

Pairwise Fairness for Ordinal Regression. - Matthäus Kleindessner; Samira Samadi; Muhammad Bilal Zafar; Krishnaram Kenthapadi; Chris Russell 001 (AISTATS, 2022)

Multiaccurate Proxies for Downstream Fairness. - Emily Diana; Wesley Gill; Michael Kearns; Krishnaram Kenthapadi; Aaron Roth 001; Saeed Sharifi-Malvajerdi (FAccT, 2022)

Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness. - Kate Donahue; Alexandra Chouldechova; Krishnaram Kenthapadi (FAccT, 2022)

A Human-Centric Perspective on Model Monitoring. - Murtuza N. Shergadwala; Himabindu Lakkaraju; Krishnaram Kenthapadi (HCOMP, 2022)

COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks. - Fan Wu 011; Linyi Li 001; Huan Zhang 001; Bhavya Kailkhura; Krishnaram Kenthapadi; Ding Zhao; Bo Li 026 (ICLR, 2022)

Generating Distributional Adversarial Examples to Evade Statistical Detectors. - Yigitcan Kaya; Muhammad Bilal Zafar; Sergül Aydöre; Nathalie Rauschmayr; Krishnaram Kenthapadi (ICML, 2022)

Model Monitoring in Practice: Lessons Learned and Open Challenges. - Krishnaram Kenthapadi; Himabindu Lakkaraju; Pradeep Natarajan; Mehrnoosh Sameki (KDD, 2022)

Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. - David Nigenda; Zohar Karnin; Muhammad Bilal Zafar; Raghu Ramesha; Alan Tan; Michele Donini; Krishnaram Kenthapadi (KDD, 2022)

Profiling Deep Learning Workloads at Scale using Amazon SageMaker. - Nathalie Rauschmayr; Sami Kama; Muhyun Kim; Miyoung Choi; Krishnaram Kenthapadi (KDD, 2022)

Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks. - Michael Lohaus; Matthäus Kleindessner; Krishnaram Kenthapadi; Francesco Locatello; Chris Russell 001 (NeurIPS, 2022)

Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases. - David Munechika; Zijie J. Wang; Jack Reidy; Joshua Rubin; Krishna Gade; Krishnaram Kenthapadi; Duen Horng Chau (IEEE VIS (Short Papers), 2022)

Designing Closed Human-in-the-loop Deferral Pipelines. - Vijay Keswani; Matthew Lease; Krishnaram Kenthapadi (CoRR, 2022)

COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks. - Fan Wu 011; Linyi Li 001; Chejian Xu; Huan Zhang 001; Bhavya Kailkhura; Krishnaram Kenthapadi; Ding Zhao; Bo Li 026 (CoRR, 2022)

Diverse Counterfactual Explanations for Anomaly Detection in Time Series. - Déborah Sulem; Michele Donini; Muhammad Bilal Zafar; Francois-Xavier Aubet; Jan Gasthaus; Tim Januschowski; Sanjiv Das; Krishnaram Kenthapadi; Cédric Archambeau (CoRR, 2022)

A Human-Centric Take on Model Monitoring. - Murtuza N. Shergadwala; Himabindu Lakkaraju; Krishnaram Kenthapadi (CoRR, 2022)

Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users. - Ana Lucic; Sheeraz Ahmad; Amanda Furtado Brinhosa; Vera Liao; Himani Agrawal; Umang Bhatt; Krishnaram Kenthapadi; Alice Xiang; Maarten de Rijke; Nicholas Drabowski (CoRR, 2022)

Designing Closed-Loop Models for Task Allocation. - Vijay Keswani; L. Elisa Celis; Krishnaram Kenthapadi; Matthew Lease (HHAI, 2023)

Generative AI meets Responsible AI: Practical Challenges and Opportunities. - Krishnaram Kenthapadi; Himabindu Lakkaraju; Nazneen Rajani (KDD, 2023)

Tutorials at The Web Conference 2023. - Valeria Fionda; Olaf Hartig; Reyhaneh Abdolazimi; Sihem Amer-Yahia; Hongzhi Chen; Xiao Chen 016; Peng Cui 001; Jeffrey Dalton 001; Xin Luna Dong; Lisette Espín-Noboa; Wenqi Fan; Manuela Fritz; et al. (WWW (Companion Volume), 2023)

Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddings. - Gyandev Gupta; Bashir Rastegarpanah; Amalendu Iyer; Joshua Rubin; Krishnaram Kenthapadi (CoRR, 2023)

Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey). - Krishnaram Kenthapadi; Mehrnoosh Sameki; Ankur Taly (KDD, 2024)

Mastering the Craft of Data Synthesis for CodeLLMs. - Meng Chen; Philip Arthur; Qianyu Feng; Cong Duy Vu Hoang; Yu-Heng Hong; Mahdi Kazemi Moghaddam; Omid Nezami; Thien Nguyen; Gioacchino Tangari; Duy Vu; Thanh Vu; Mark Johnson 001; et al. (CoRR, 2024)

Mastering the Craft of Data Synthesis for CodeLLMs. - Meng Chen; Philip Arthur; Qianyu Feng; Cong Duy Vu Hoang; Yu-Heng Hong; Mahdi Kazemi Moghaddam; Omid Nezami; Duc Thien Nguyen; Gioacchino Tangari; Duy Vu; Thanh Vu; Mark Johnson 001; et al. (NAACL (Long Papers), 2025)

Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey. - Bo Ni; Zheyuan Liu 010; Leyao Wang; Yongjia Lei; Yuying Zhao; Xueqi Cheng; Qingkai Zeng 001; Luna Dong; Yinglong Xia; Krishnaram Kenthapadi; Ryan A. Rossi; Franck Dernoncourt; et al. (CoRR, 2025)

RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification. - Praphul Singh; Charlotte Dzialo; Jangwon Kim; Sumana Srivatsa; Irfan Ullah; Sri Gadde; Krishnaram Kenthapadi (CoRR, 2025)

Permissioned LLMs: Enforcing Access Control in Large Language Models. - Bargav Jayaraman; Virendra J. Marathe; Hamid Mozaffari; William F. Shen; Krishnaram Kenthapadi (CoRR, 2025)