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2020 – today
- 2024
[c75]Krishnaram Kenthapadi
, Mehrnoosh Sameki, Ankur Taly:
Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey). KDD 2024: 6523-6533- [i39]Krishnaram Kenthapadi, Mehrnoosh Sameki, Ankur Taly:
Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey). CoRR abs/2407.12858 (2024) - [i38]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, Krishnaram Kenthapadi, Don Dharmasiri, Long Duong, Yuan-Fang Li:
Mastering the Craft of Data Synthesis for CodeLLMs. CoRR abs/2411.00005 (2024) - 2023
- [c74]Vijay Keswani, L. Elisa Celis, Krishnaram Kenthapadi, Matthew Lease:
Designing Closed-Loop Models for Task Allocation. HHAI 2023: 17-32 - [c73]Krishnaram Kenthapadi, Himabindu Lakkaraju, Nazneen Rajani:
Generative AI meets Responsible AI: Practical Challenges and Opportunities. KDD 2023: 5805-5806 - [c72]Valeria Fionda, Olaf Hartig, Reyhaneh Abdolazimi, Sihem Amer-Yahia, Hongzhi Chen, Xiao Chen, Peng Cui, Jeffrey Dalton, Xin Luna Dong, Lisette Espín-Noboa, Wenqi Fan, Manuela Fritz, Quan Gan, Jingtong Gao, Xiaojie Guo, Torsten Hahmann, Jiawei Han, Soyeon Caren Han, Estevam Hruschka, Liang Hu, Jiaxin Huang, Utkarshani Jaimini, Olivier Jeunen, Yushan Jiang, Fariba Karimi, George Karypis, Krishnaram Kenthapadi, Himabindu Lakkaraju, Hady W. Lauw, Thai Le, Trung-Hoang Le, Dongwon Lee, Geon Lee, Liat Levontin, Cheng-Te Li, Haoyang Li, Ying Li, Jay Chiehen Liao, Qidong Liu, Usha Lokala, Ben London, Siqu Long, Hande Küçük-McGinty, Yu Meng, Seungwhan Moon, Usman Naseem, Pradeep Natarajan, Behrooz Omidvar-Tehrani, Zijie Pan, Devesh Parekh, Jian Pei, Tiago Peixoto, Steven Pemberton, Josiah Poon, Filip Radlinski, Federico Rossetto, Kaushik Roy, Aghiles Salah, Mehrnoosh Sameki, Amit P. Sheth, Cogan Shimizu, Kijung Shin, Dongjin Song, Julia Stoyanovich, Dacheng Tao, Johanne Trippas, Quoc Truong, Yu-Che Tsai, Adaku Uchendu, Bram van den Akker, Lin Wang, Minjie Wang, Shoujin Wang, Xin Wang, Ingmar Weber, Henry Weld, Lingfei Wu, Da Xu, Yifan Ethan Xu, Shuyuan Xu, Bo Yang, Ke Yang, Elad Yom-Tov, Jaemin Yoo, Zhou Yu, Reza Zafarani, Hamed Zamani, Meike Zehlike, Qi Zhang, Xikun Zhang, Yongfeng Zhang, Yu Zhang, Zheng Zhang, Liang Zhao, Xiangyu Zhao, Wenwu Zhu:
Tutorials at The Web Conference 2023. WWW (Companion Volume) 2023: 648-658 - [i37]Vijay Keswani, L. Elisa Celis, Krishnaram Kenthapadi, Matthew Lease:
Designing Closed-Loop Models for Task Allocation. CoRR abs/2305.19864 (2023) - [i36]Gyandev Gupta, Bashir Rastegarpanah, Amalendu Iyer, Joshua Rubin, Krishnaram Kenthapadi:
Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddings. CoRR abs/2312.02337 (2023) - 2022
- [c71]Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell:
Pairwise Fairness for Ordinal Regression. AISTATS 2022: 3381-3417 - [c70]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi:
Multiaccurate Proxies for Downstream Fairness. FAccT 2022: 1207-1239 - [c69]Kate Donahue, Alexandra Chouldechova, Krishnaram Kenthapadi:
Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness. FAccT 2022: 1639-1656 - [c68]Murtuza N. Shergadwala, Himabindu Lakkaraju, Krishnaram Kenthapadi:
A Human-Centric Perspective on Model Monitoring. HCOMP 2022: 173-183 - [c67]Fan Wu, Linyi Li, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li:
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks. ICLR 2022 - [c66]Yigitcan Kaya, Muhammad Bilal Zafar, Sergül Aydöre, Nathalie Rauschmayr, Krishnaram Kenthapadi:
Generating Distributional Adversarial Examples to Evade Statistical Detectors. ICML 2022: 10895-10911 - [c65]David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi:
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. KDD 2022: 3671-3681 - [c64]Nathalie Rauschmayr, Sami Kama, Muhyun Kim, Miyoung Choi, Krishnaram Kenthapadi:
Profiling Deep Learning Workloads at Scale using Amazon SageMaker. KDD 2022: 3801-3809 - [c63]Krishnaram Kenthapadi, Himabindu Lakkaraju, Pradeep Natarajan, Mehrnoosh Sameki:
Model Monitoring in Practice: Lessons Learned and Open Challenges. KDD 2022: 4800-4801 - [c62]Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell:
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks. NeurIPS 2022 - [c61]David Munechika, Zijie J. Wang, Jack Reidy, Joshua Rubin, Krishna Gade, Krishnaram Kenthapadi, Duen Horng Chau:
Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases. IEEE VIS (Short Papers) 2022: 45-49 - [i35]Vijay Keswani, Matthew Lease, Krishnaram Kenthapadi:
Designing Closed Human-in-the-loop Deferral Pipelines. CoRR abs/2202.04718 (2022) - [i34]Kate Donahue, Alexandra Chouldechova, Krishnaram Kenthapadi:
Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness. CoRR abs/2202.08821 (2022) - [i33]Fan Wu, Linyi Li, Chejian Xu, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li:
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks. CoRR abs/2203.08398 (2022) - [i32]Déborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cédric Archambeau:
Diverse Counterfactual Explanations for Anomaly Detection in Time Series. CoRR abs/2203.11103 (2022) - [i31]Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell:
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks. CoRR abs/2204.04440 (2022) - [i30]Murtuza N. Shergadwala, Himabindu Lakkaraju, Krishnaram Kenthapadi:
A Human-Centric Take on Model Monitoring. CoRR abs/2206.02868 (2022) - [i29]David Munechika, Zijie J. Wang, Jack Reidy, Joshua Rubin, Krishna Gade, Krishnaram Kenthapadi, Duen Horng Chau:
Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases. CoRR abs/2206.12540 (2022) - [i28]Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa, Vera Liao, Himani Agrawal, Umang Bhatt, Krishnaram Kenthapadi, Alice Xiang, Maarten de Rijke, Nicholas Drabowski:
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users. CoRR abs/2207.02726 (2022) - 2021
- [c60]Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi:
On the Lack of Robust Interpretability of Neural Text Classifiers. ACL/IJCNLP (Findings) 2021: 3730-3740 - [c59]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth:
Minimax Group Fairness: Algorithms and Experiments. AIES 2021: 66-76 - [c58]Vijay Keswani, Matthew Lease, Krishnaram Kenthapadi:
Towards Unbiased and Accurate Deferral to Multiple Experts. AIES 2021: 154-165 - [c57]Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau:
Fair Bayesian Optimization. AIES 2021: 854-863 - [c56]Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva:
Differentially Private Query Release Through Adaptive Projection. ICML 2021: 457-467 - [c55]Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Amaresh Ankit Siva, ErhYuan Tsai, Keerthan Vasist, Pinar Yilmaz, Muhammad Bilal Zafar, Sanjiv Das, Kevin Haas, Tyler Hill, Krishnaram Kenthapadi:
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud. KDD 2021: 2974-2983 - [c54]Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau:
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. KDD 2021: 3463-3471 - [c53]Nathalie Rauschmayr, Vikas Kumar, Rahul Huilgol, Andrea Olgiati, Satadal Bhattacharjee, Nihal Harish, Vandana Kannan, Amol Lele, Anirudh Acharya, Jared Nielsen, Lakshmi Ramakrishnan, Ishan Bhatt, Kohen Chia, Neelesh Dodda, Zhihan Li, Jiacheng Gu, Miyoung Choi, Balajee Nagarajan, Jeffrey Geevarghese, Denis Davydenko, Sifei Li, Lu Huang, Edward Kim, Tyler Hill, Krishnaram Kenthapadi:
Amazon SageMaker Debugger: A System for Real-Time Insights into Machine Learning Model Training. MLSys 2021 - [i27]Dylan Slack, Nathalie Rauschmayr, Krishnaram Kenthapadi:
Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy. CoRR abs/2102.06162 (2021) - [i26]Vijay Keswani, Matthew Lease, Krishnaram Kenthapadi:
Towards Unbiased and Accurate Deferral to Multiple Experts. CoRR abs/2102.13004 (2021) - [i25]Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva:
Differentially Private Query Release Through Adaptive Projection. CoRR abs/2103.06641 (2021) - [i24]Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell:
Pairwise Fairness for Ordinal Regression. CoRR abs/2105.03153 (2021) - [i23]Zeinab S. Jalali, Krishnaram Kenthapadi, Sucheta Soundarajan:
On Measuring the Diversity of Organizational Networks. CoRR abs/2105.06929 (2021) - [i22]Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi:
On the Lack of Robust Interpretability of Neural Text Classifiers. CoRR abs/2106.04631 (2021) - [i21]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi:
Multiaccurate Proxies for Downstream Fairness. CoRR abs/2107.04423 (2021) - [i20]Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Amaresh Ankit Siva, ErhYuan Tsai, Keerthan Vasist, Pinar Yilmaz, Muhammad Bilal Zafar, Sanjiv Das, Kevin Haas, Tyler Hill, Krishnaram Kenthapadi:
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud. CoRR abs/2109.03285 (2021) - [i19]David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi:
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. CoRR abs/2111.13657 (2021) - [i18]Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini, Cédric Archambeau, Felix Biessmann, Sanjiv Ranjan Das, Krishnaram Kenthapadi:
More Than Words: Towards Better Quality Interpretations of Text Classifiers. CoRR abs/2112.12444 (2021) - 2020
- [c52]Sriram Vasudevan, Krishnaram Kenthapadi:
LiFT: A Scalable Framework for Measuring Fairness in ML Applications. CIKM 2020: 2773-2780 - [c51]Kathy Baxter, Yoav Schlesinger, Sarah Aerni, Lewis J. Baker, Julie Dawson, Krishnaram Kenthapadi, Isabel M. Kloumann, Hanna M. Wallach:
Bridging the gap from AI ethics research to practice. FAT* 2020: 682 - [c50]Krishna Gade, Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal, Ankur Taly:
Explainable AI in industry: practical challenges and lessons learned: implications tutorial. FAT* 2020: 699 - [c49]Cyrus DiCiccio, Sriram Vasudevan, Kinjal Basu, Krishnaram Kenthapadi, Deepak Agarwal:
Evaluating Fairness Using Permutation Tests. KDD 2020: 1467-1477 - [c48]Krishna Gade, Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal, Ankur Taly:
Explainable AI in Industry: Practical Challenges and Lessons Learned. WWW (Companion Volume) 2020: 303-304 - [i17]Valerio Perrone, Michele Donini, Krishnaram Kenthapadi, Cédric Archambeau:
Fair Bayesian Optimization. CoRR abs/2006.05109 (2020) - [i16]G. Roshan Lal, Sahin Cem Geyik, Krishnaram Kenthapadi:
Fairness-Aware Online Personalization. CoRR abs/2007.15270 (2020) - [i15]Sriram Vasudevan, Krishnaram Kenthapadi:
LiFT: A Scalable Framework for Measuring Fairness in ML Applications. CoRR abs/2008.07433 (2020) - [i14]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth:
Convergent Algorithms for (Relaxed) Minimax Fairness. CoRR abs/2011.03108 (2020) - [i13]Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau:
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. CoRR abs/2012.08489 (2020)
2010 – 2019
- 2019
- [c47]Maria De-Arteaga, Alexey Romanov, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Adam Tauman Kalai:
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. FAT 2019: 120-128 - [c46]Sahin Cem Geyik, Stuart Ambler, Krishnaram Kenthapadi:
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search. KDD 2019: 2221-2231 - [c45]Krishna Gade, Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal, Ankur Taly:
Explainable AI in Industry. KDD 2019: 3203-3204 - [c44]Sarah Bird, Ben Hutchinson, Krishnaram Kenthapadi, Emre Kiciman, Margaret Mitchell:
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned. KDD 2019: 3205-3206 - [c43]Alexey Romanov, Maria De-Arteaga, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Kalai:
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes. NAACL-HLT (1) 2019: 4187-4195 - [c42]Sarah Bird, Krishnaram Kenthapadi, Emre Kiciman, Margaret Mitchell:
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned. WSDM 2019: 834-835 - [c41]Krishnaram Kenthapadi, Ilya Mironov, Abhradeep Guha Thakurta:
Privacy-preserving Data Mining in Industry. WSDM 2019: 840-841 - [c40]Sarah Bird, Ben Hutchinson, Krishnaram Kenthapadi, Emre Kiciman, Margaret Mitchell:
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned. WWW (Companion Volume) 2019: 1297-1298 - [c39]Krishnaram Kenthapadi, Ilya Mironov, Abhradeep Thakurta:
Privacy-preserving Data Mining in Industry. WWW (Companion Volume) 2019: 1308-1310 - [i12]Maria De-Arteaga, Alexey Romanov, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Adam Tauman Kalai:
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. CoRR abs/1901.09451 (2019) - [i11]Alexey Romanov, Maria De-Arteaga, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Tauman Kalai:
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes. CoRR abs/1904.05233 (2019) - [i10]Sahin Cem Geyik, Stuart Ambler, Krishnaram Kenthapadi:
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search. CoRR abs/1905.01989 (2019) - 2018
- [c38]Krishnaram Kenthapadi, Thanh T. L. Tran:
PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn. CIKM 2018: 2183-2191 - [c37]Rohan Ramanath, Hakan Inan, Gungor Polatkan, Bo Hu, Qi Guo, Cagri Ozcaglar, Xianren Wu, Krishnaram Kenthapadi, Sahin Cem Geyik:
Towards Deep and Representation Learning for Talent Search at LinkedIn. CIKM 2018: 2253-2261 - [c36]Xi Chen, Yiqun Liu, Liang Zhang, Krishnaram Kenthapadi:
How LinkedIn Economic Graph Bonds Information and Product: Applications in LinkedIn Salary. KDD 2018: 120-129 - [c35]Sahin Cem Geyik, Qi Guo, Bo Hu, Cagri Ozcaglar, Ketan Thakkar, Xianren Wu, Krishnaram Kenthapadi:
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned. SIGIR 2018: 1353-1354 - [i9]Xi Chen, Yiqun Liu, Liang Zhang, Krishnaram Kenthapadi:
How LinkedIn Economic Graph Bonds Information and Product: Applications in LinkedIn Salary. CoRR abs/1806.09063 (2018) - [i8]Rohan Ramanath, Hakan Inan, Gungor Polatkan, Bo Hu, Qi Guo, Cagri Ozcaglar, Xianren Wu, Krishnaram Kenthapadi, Sahin Cem Geyik:
Towards Deep and Representation Learning for Talent Search at LinkedIn. CoRR abs/1809.06473 (2018) - [i7]Sahin Cem Geyik, Qi Guo, Bo Hu, Cagri Ozcaglar, Ketan Thakkar, Xianren Wu, Krishnaram Kenthapadi:
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned. CoRR abs/1809.06481 (2018) - [i6]Krishnaram Kenthapadi, Thanh T. L. Tran:
PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn. CoRR abs/1809.07754 (2018) - 2017
- [c34]Krishnaram Kenthapadi, Stuart Ambler, Liang Zhang, Deepak Agarwal:
Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary. CIKM 2017: 447-455 - [c33]Fedor Borisyuk, Liang Zhang, Krishnaram Kenthapadi:
LiJAR: A System for Job Application Redistribution towards Efficient Career Marketplace. KDD 2017: 1397-1406 - [c32]Krishnaram Kenthapadi, Ahsan Chudhary, Stuart Ambler:
LinkedIn Salary: A System for Secure Collection and Presentation of Structured Compensation Insights to Job Seekers. PAC 2017: 13-24 - [c31]Krishnaram Kenthapadi, Benjamin Le, Ganesh Venkataraman:
Personalized Job Recommendation System at LinkedIn: Practical Challenges and Lessons Learned. RecSys 2017: 346-347 - [c30]Dhruv Arya, Ganesh Venkataraman, Aman Grover, Krishnaram Kenthapadi:
Candidate Selection for Large Scale Personalized Search and Recommender Systems. SIGIR 2017: 1391-1393 - [i5]Krishnaram Kenthapadi, Stuart Ambler, Liang Zhang, Deepak Agarwal:
Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary. CoRR abs/1703.09845 (2017) - [i4]Krishnaram Kenthapadi, Ahsan Chudhary, Stuart Ambler:
LinkedIn Salary: A System for Secure Collection and Presentation of Structured Compensation Insights to Job Seekers. CoRR abs/1705.06976 (2017) - [i3]Jian Wang, Krishnaram Kenthapadi, Kaushik Rangadurai, David Hardtke:
Dionysius: A Framework for Modeling Hierarchical User Interactions in Recommender Systems. CoRR abs/1706.03849 (2017) - 2016
- [c29]Fedor Borisyuk, Krishnaram Kenthapadi, David Stein, Bo Zhao:
CaSMoS: A Framework for Learning Candidate Selection Models over Structured Queries and Documents. KDD 2016: 441-450 - 2014
- [c28]Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi:
Similarity Search using Concept Graphs. CIKM 2014: 719-728 - [c27]James Cook, Abhimanyu Das, Krishnaram Kenthapadi, Nina Mishra:
Ranking Twitter discussion groups. COSN 2014: 177-190 - [c26]Marios Kokkodis, Anitha Kannan, Krishnaram Kenthapadi:
Assigning Educational Videos at Appropriate Locations in Textbooks. EDM 2014: 201-204 - [c25]Rakesh Agrawal, Maria Christoforaki, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi, Adith Swaminathan:
Mining Videos from the Web for Electronic Textbooks. ICFCA 2014: 219-234 - [c24]Marios Kokkodis, Anitha Kannan, Krishnaram Kenthapadi:
Assigning videos to textbooks at appropriate granularity. L@S 2014: 199-200 - [c23]Rakesh Agrawal, M. Hanif Jhaveri, Krishnaram Kenthapadi:
Evaluating educational interventions at scale. L@S 2014: 207-208 - [e1]Anitha Kannan, Krishnaram Kenthapadi, Shankar Kalyanaraman, Marshini Chetty, Vanessa Frías-Martínez:
Proceedings of the Fifth ACM Symposium on Computing for Development, ACM DEV 2014, San Jose, CA, USA, December 5-6, 2014. ACM 2014, ISBN 978-1-4503-2936-1 [contents] - 2013
- [j4]Krishnaram Kenthapadi, Nina Mishra, Kobbi Nissim:
Denials leak information: Simulatable auditing. J. Comput. Syst. Sci. 79(8): 1322-1340 (2013) - [j3]Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, Nina Mishra:
Privacy via the Johnson-Lindenstrauss Transform. J. Priv. Confidentiality 5(1) (2013) - [c22]Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi:
Studying from electronic textbooks. CIKM 2013: 1715-1720 - [c21]James Cook, Krishnaram Kenthapadi, Nina Mishra:
Group chats on Twitter. WWW 2013: 225-236 - 2012
- [c20]Rakesh Agrawal, Sunandan Chakraborty, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi:
Quality of textbooks: an empirical study. ACM DEV 2012: 16:1 - [c19]Rakesh Agrawal, Sunandan Chakraborty, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi:
Empowering authors to diagnose comprehension burden in textbooks. KDD 2012: 967-975 - [c18]Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi:
Electronic Textbooks and Data Mining. WAIM 2012: 20-21 - [c17]Marc Najork, Dennis Fetterly, Alan Halverson, Krishnaram Kenthapadi, Sreenivas Gollapudi:
Of hammers and nails: an empirical comparison of three paradigms for processing large graphs. WSDM 2012: 103-112 - [i2]Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, Nina Mishra:
Privacy via the Johnson-Lindenstrauss Transform. CoRR abs/1204.2606 (2012) - 2011
- [j2]Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi:
Data mining for improving textbooks. SIGKDD Explor. 13(2): 7-19 (2011) - [c16]Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi:
Enriching textbooks with images. CIKM 2011: 1847-1856 - [c15]Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi:
Enriching Education through Data Mining. PReMI 2011: 1-2 - [c14]Shuai Ding, Sreenivas Gollapudi, Samuel Ieong, Krishnaram Kenthapadi, Alexandros Ntoulas:
Indexing strategies for graceful degradation of search quality. SIGIR 2011: 575-584 - [c13]Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi:
Identifying enrichment candidates in textbooks. WWW (Companion Volume) 2011: 483-492 - 2010
- [j1]Gagan Aggarwal, Rina Panigrahy, Tomás Feder, Dilys Thomas, Krishnaram Kenthapadi, Samir Khuller, An Zhu:
Achieving anonymity via clustering. ACM Trans. Algorithms 6(3): 49:1-49:19 (2010) - [c12]Rakesh Agrawal, Sreenivas Gollapudi, Krishnaram Kenthapadi, Nitish Srivastava, Raja Velu:
Enriching textbooks through data mining. ACM DEV 2010: 19
2000 – 2009
- 2009
- [c11]Rakesh Agrawal, Alan Halverson, Krishnaram Kenthapadi, Nina Mishra, Panayiotis Tsaparas:
Generating labels from clicks. WSDM 2009: 172-181 - [c10]Aleksandra Korolova, Krishnaram Kenthapadi, Nina Mishra, Alexandros Ntoulas:
Releasing search queries and clicks privately. WWW 2009: 171-180 - 2008
- [p1]Shubha U. Nabar, Krishnaram Kenthapadi, Nina Mishra, Rajeev Motwani:
A Survey of Query Auditing Techniques for Data Privacy. Privacy-Preserving Data Mining 2008: 415-431 - 2006
- [b1]Krishnaram Kenthapadi:
Models and algorithms for data privacy. Stanford University, USA, 2006 - [c9]Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, Moni Naor:
Our Data, Ourselves: Privacy Via Distributed Noise Generation. EUROCRYPT 2006: 486-503 - [c8]Gagan Aggarwal, Tomás Feder, Krishnaram Kenthapadi, Samir Khuller, Rina Panigrahy, Dilys Thomas, An Zhu:
Achieving anonymity via clustering. PODS 2006: 153-162 - [c7]Krishnaram Kenthapadi, Rina Panigrahy:
Balanced allocation on graphs. SODA 2006: 434-443 - [c6]Shubha U. Nabar, Bhaskara Marthi, Krishnaram Kenthapadi, Nina Mishra, Rajeev Motwani:
Towards Robustness in Query Auditing. VLDB 2006: 151-162 - 2005
- [c5]Gagan Aggarwal, Mayank Bawa, Prasanna Ganesan, Hector Garcia-Molina, Krishnaram Kenthapadi, Rajeev Motwani, Utkarsh Srivastava, Dilys Thomas, Ying Xu:
Two Can Keep A Secret: A Distributed Architecture for Secure Database Services. CIDR 2005: 186-199 - [c4]Gagan Aggarwal, Tomás Feder, Krishnaram Kenthapadi, Rajeev Motwani, Rina Panigrahy, Dilys Thomas, An Zhu:
Anonymizing Tables. ICDT 2005: 246-258 - [c3]Krishnaram Kenthapadi, Nina Mishra, Kobbi Nissim:
Simulatable auditing. PODS 2005: 118-127 - [c2]Krishnaram Kenthapadi, Gurmeet Singh Manku:
Decentralized algorithms using both local and random probes for P2P load balancing. SPAA 2005: 135-144 - [i1]Krishnaram Kenthapadi, Rina Panigrahy:
Balanced Allocation on Graphs. CoRR abs/cs/0510086 (2005) - 2004
- [c1]Gagan Aggarwal, Mayank Bawa, Prasanna Ganesan, Hector Garcia-Molina, Krishnaram Kenthapadi, Nina Mishra, Rajeev Motwani, Utkarsh Srivastava, Dilys Thomas, Jennifer Widom, Ying Xu:
Vision Paper: Enabling Privacy for the Paranoids. VLDB 2004: 708-719
Coauthor Index
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