Deepak Agarwal
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
63
Venues
19
Active years
2004–2025
Best venue rank
A*
Where they publish
Papers
63 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | COMSNETS | On Demand Transmissions of Common Signals for Network Energy Saving. | Deepak Agarwal, Deepak PM, Sheetal Kalyani, Ramya TR, J. Klutto Milleth |
| 2020 | CIKM | DeText: A Deep Text Ranking Framework with BERT. | Weiwei Guo, Xiaowei Liu, Sida Wang, Huiji Gao, Ananth Sankar, Zimeng Yang, Qi Guo, Liang Zhang, Bo Long, Bee-Chung Chen, Deepak Agarwal |
| 2020 | EDM | Dynamic knowledge tracing through data driven recency weights. | Deepak Agarwal, Ryan Baker, Anupama Muraleedharan |
| 2020 | KDD | Evaluating Fairness Using Permutation Tests. | Cyrus DiCiccio, Sriram Vasudevan, Kinjal Basu, Krishnaram Kenthapadi, Deepak Agarwal |
| 2019 | KDD | Deep Natural Language Processing for Search and Recommender Systems. | Weiwei Guo, Huiji Gao, Jun Shi, Bo Long, Liang Zhang, Bee-Chung Chen, Deepak Agarwal |
| 2018 | EDM | Contextual Derivation of Stable BKT Parameters for Analysing Content Efficacy. | Deepak Agarwal, Nishant Babel, Ryan S. Baker |
| 2018 | KDD | Online Parameter Selection for Web-based Ranking Problems. | Deepak Agarwal, Kinjal Basu, Souvik Ghosh, Ying Xuan, Yang Yang, Liang Zhang |
| 2017 | CIKM | Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary. | Krishnaram Kenthapadi, Stuart Ambler, Liang Zhang, Deepak Agarwal |
| 2017 | KDD | Data-Driven Reserve Prices for Social Advertising Auctions at LinkedIn. | Tingting Cui, Lijun Peng, David Pardoe, Kun Liu, Deepak Agarwal, Deepak Kumar |
| 2017 | KDD | The Future of Artificially Intelligent Assistants. | Muthu Muthukrishnan, Andrew Tomkins, Larry P. Heck, Alborz Geramifard, Deepak Agarwal |
| 2017 | WSDM | Social Incentive Optimization in Online Social Networks. | Guangde Chen, Bee-Chung Chen, Deepak Agarwal |
| 2016 | COMAD | Recommendations in the context of a Social Network. | Deepak Agarwal |
| 2016 | KDD | Ranking Universities Based on Career Outcomes of Graduates. | Navneet Kapur, Nikita I. Lytkin, Bee-Chung Chen, Deepak Agarwal, Igor Perisic |
| 2016 | KDD | An Empirical Study on Recommendation with Multiple Types of Feedback. | Liang Tang, Bo Long, Bee-Chung Chen, Deepak Agarwal |
| 2016 | KDD | GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction. | XianXing Zhang, Yitong Zhou, Yiming Ma, Bee-Chung Chen, Liang Zhang, Deepak Agarwal |
| 2016 | PAM | Scout: A Point of Presence Recommendation System Using Real User Monitoring Data. | Yang Yang, Liang Zhang, Ritesh Maheshwari, Zaid Ali Kahn, Deepak Agarwal, Sanjay Dubey |
| 2016 | RecSys | Tutorial: Lessons Learned from Building Real-life Recommender Systems. | Xavier Amatriain, Deepak Agarwal |
| 2015 | KDD | Scaling Machine Learning and Statistics for Web Applications. | Deepak Agarwal |
| 2015 | KDD | Personalizing LinkedIn Feed. | Deepak Agarwal, Bee-Chung Chen, Qi He, Zhenhao Hua, Guy Lebanon, Yiming Ma, Pannagadatta Shivaswamy, Hsiao-Ping Tseng, Jaewon Yang, Liang Zhang |
| 2015 | WWW | Constrained Optimization for Homepage Relevance. | Deepak Agarwal, Shaunak Chatterjee, Yang Yang, Liang Zhang |
| 2015 | SIGMOD | Amazon Redshift and the Case for Simpler Data Warehouses. | Anurag Gupta, Deepak Agarwal, Derek Tan, Jakub Kulesza, Rahul Pathak, Stefano Stefani, Vidhya Srinivasan |
| 2014 | KDD | Activity ranking in LinkedIn feed. | Deepak Agarwal, Bee-Chung Chen, Rupesh Gupta, Joshua Hartman, Qi He, Anand Iyer, Sumanth Kolar, Yiming Ma, Pannagadatta Shivaswamy, Ajit Singh, Liang Zhang |
| 2014 | KDD | Budget pacing for targeted online advertisements at LinkedIn. | Deepak Agarwal, Souvik Ghosh, Kai Wei, Siyu You |
| 2014 | WSDM | LASER: a scalable response prediction platform for online advertising. | Deepak Agarwal, Bo Long, Jonathan Traupman, Doris Xin, Liang Zhang |
| 2013 | CIKM | Computational advertising: the linkedin way. | Deepak Agarwal |
| 2013 | CIKM | Recommending items to users: an explore/exploit perspective. | Deepak Agarwal |
| 2013 | CIKM | Automatic ad format selection via contextual bandits. | Liang Tang, Rmer Rosales, Ajit Singh, Deepak Agarwal |
| 2013 | KDD | Estimating sharer reputation via social data calibration. | Jaewon Yang, Bee-Chung Chen, Deepak Agarwal |
| 2013 | WWW | Organizational overlap on social networks and its applications. | Cho-Jui Hsieh, Mitul Tiwari, Deepak Agarwal, Xinyi (Lisa) Huang, Sam Shah |
| 2012 | CIKM | Multi-faceted ranking of news articles using post-read actions. | Deepak Agarwal, Bee-Chung Chen, Xuanhui Wang |
| 2012 | WWW | Targeting converters for new campaigns through factor models. | Deepak Agarwal, Sandeep Pandey, Vanja Josifovski |
| 2012 | SIGIR | Personalized click shaping through lagrangian duality for online recommendation. | Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, Xuanhui Wang |
| 2012 | WSDM | Fast top-k retrieval for model based recommendation. | Deepak Agarwal, Maxim Gurevich |
| 2012 | WSDM | Finding the right consumer: optimizing for conversion in display advertising campaigns. | Yandong Liu, Sandeep Pandey, Deepak Agarwal, Vanja Josifovski |
| 2011 | EMNLP | Personalized Recommendation of User Comments via Factor Models. | Deepak Agarwal, Bee-Chung Chen, Bo Pang |
| 2011 | KDD | Click shaping to optimize multiple objectives. | Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, Xuanhui Wang |
| 2011 | KDD | Localized factor models for multi-context recommendation. | Deepak Agarwal, Bee-Chung Chen, Bo Long |
| 2011 | KDD | Temporal multi-hierarchy smoothing for estimating rates of rare events. | Nagaraj Kota, Deepak Agarwal |
| 2011 | KDD | Multiple domain user personalization. | Yucheng Low, Deepak Agarwal, Alexander J. Smola |
| 2011 | KDD | Response prediction using collaborative filtering with hierarchies and side-information. | Aditya Krishna Menon, Krishna Prasad Chitrapura, Sachin Garg, Deepak Agarwal, Nagaraj Kota |
| 2011 | RecSys | Generalizing matrix factorization through flexible regression priors. | Liang Zhang, Deepak Agarwal, Bee-Chung Chen |
| 2011 | SIGMOD | Latent OLAP: data cubes over latent variables. | Deepak Agarwal, Bee-Chung Chen |
| 2010 | KDD | Estimating rates of rare events with multiple hierarchies through scalable log-linear models. | Deepak Agarwal, Rahul Agrawal, Rajiv Khanna, Nagaraj Kota |
| 2010 | KDD | Fast online learning through offline initialization for time-sensitive recommendation. | Deepak Agarwal, Bee-Chung Chen, Pradheep Elango |
| 2010 | SIGMOD | Forecasting high-dimensional data. | Deepak Agarwal, Datong Chen, Long-ji Lin, Jayavel Shanmugasundaram, Erik Vee |
| 2010 | WSDM | fLDA: matrix factorization through latent dirichlet allocation. | Deepak Agarwal, Bee-Chung Chen |
| 2009 | CIKM | Translating relevance scores to probabilities for contextual advertising. | Deepak Agarwal, Evgeniy Gabrilovich, Robert J. Hall, Vanja Josifovski, Rajiv Khanna |
| 2009 | ECIR | Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering. | Yashar Moshfeghi, Deepak Agarwal, Benjamin Piwowarski, Joemon M. Jose |
| 2009 | ICDM | Explore/Exploit Schemes for Web Content Optimization. | Deepak Agarwal, Bee-Chung Chen, Pradheep Elango |
| 2009 | KDD | Regression-based latent factor models. | Deepak Agarwal, Bee-Chung Chen |
| 2009 | RecSys | A spatio-temporal approach to collaborative filtering. | Zhengdong Lu, Deepak Agarwal, Inderjit S. Dhillon |
| 2009 | WWW | Spatio-temporal models for estimating click-through rate. | Deepak Agarwal, Bee-Chung Chen, Pradheep Elango |
| 2008 | WWW | Contextual advertising by combining relevance with click feedback. | Deepayan Chakrabarti, Deepak Agarwal, Vanja Josifovski |
| 2007 | ICDE | Parsimonious Explanations of Change in Hierarchical Data. | Dhiman Barman, Flip Korn, Divesh Srivastava, Dimitrios Gunopulos, Neal E. Young, Deepak Agarwal |
| 2007 | ICML | Multi-armed bandit problems with dependent arms. | Sandeep Pandey, Deepayan Chakrabarti, Deepak Agarwal |
| 2007 | KDD | Estimating rates of rare events at multiple resolutions. | Deepak Agarwal, Andrei Z. Broder, Deepayan Chakrabarti, Dejan Diklic, Vanja Josifovski, Mayssam Sayyadian |
| 2007 | KDD | Efficient and effective explanation of change in hierarchical summaries. | Deepak Agarwal, Dhiman Barman, Dimitrios Gunopulos, Neal E. Young, Flip Korn, Divesh Srivastava |
| 2007 | KDD | Predictive discrete latent factor models for large scale dyadic data. | Deepak Agarwal, Srujana Merugu |
| 2007 | SDM | Bandits for Taxonomies: A Model-based Approach. | Sandeep Pandey, Deepak Agarwal, Deepayan Chakrabarti, Vanja Josifovski |
| 2006 | KDD | Spatial scan statistics: approximations and performance study. | Deepak Agarwal, Andrew McGregor, Jeff M. Phillips, Suresh Venkatasubramanian, Zhengyuan Zhu |
| 2006 | SODA | The hunting of the bump: on maximizing statistical discrepancy. | Deepak Agarwal, Jeff M. Phillips, Suresh Venkatasubramanian |
| 2005 | KDD | Tuning representations of dynamic network data. | Shawndra Hill, Deepak Agarwal, Robert M. Bell, Chris Volinsky |
| 2004 | Interspeech | Mining customer care dialogs for "daily news". | Shona Douglas, Deepak Agarwal, Tirso Alonso, Robert M. Bell, Mazin G. Rahim, Deborah F. Swayne, Chris Volinsky |