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Deepak Agarwal
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2020 – today
- 2022
- [c70]Vishal Gupta, Maninder Singh, Rajeev Gupta, Basant Kumar, Deepak Agarwal:
Development of an Automated Algorithm to Quantify Optic Nerve Diameter Using Ultrasound Measures: Implications for Optic Neuropathies. CVMI 2022: 283-295 - [e3]Frédérique Laforest, Raphaël Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman
, Lionel Médini:
WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022. ACM 2022, ISBN 978-1-4503-9096-5 [contents] - [e2]Frédérique Laforest, Raphaël Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman
Companion of The Web Conference 2022, Virtual Event / Lyon, France, April 25 - 29, 2022. ACM 2022, ISBN 978-1-4503-9130-6 [contents] - 2020
- [c69]Weiwei Guo, Xiaowei Liu, Sida Wang, Huiji Gao, Ananth Sankar, Zimeng Yang, Qi Guo, Liang Zhang, Bo Long, Bee-Chung Chen, Deepak Agarwal:
DeText: A Deep Text Ranking Framework with BERT. CIKM 2020: 2509-2516 - [c68]Deepak Agarwal, Ryan Baker, Anupama Muraleedharan:
Dynamic knowledge tracing through data driven recency weights. EDM 2020 - [c67]Cyrus DiCiccio, Sriram Vasudevan, Kinjal Basu, Krishnaram Kenthapadi, Deepak Agarwal:
Evaluating Fairness Using Permutation Tests. KDD 2020: 1467-1477 - [i6]Preetam Nandy, Amir Sepehri, Kinjal Basu, Heloise Logan, Deepak Agarwal, Noureddine El Karoui:
Scalable Assessment and Mitigation Strategies for Fairness in Rankings. CoRR abs/2006.11350 (2020) - [i5]Weiwei Guo, Xiaowei Liu, Sida Wang, Huiji Gao, Ananth Sankar, Zimeng Yang, Qi Guo, Liang Zhang, Bo Long, Bee-Chung Chen, Deepak Agarwal:
DeText: A Deep Text Ranking Framework with BERT. CoRR abs/2008.02460 (2020)
2010 – 2019
- 2019
- [c66]Weiwei Guo, Huiji Gao, Jun Shi, Bo Long, Liang Zhang, Bee-Chung Chen, Deepak Agarwal:
Deep Natural Language Processing for Search and Recommender Systems. KDD 2019: 3199-3200 - [c65]Haripriya Valayaputtur, Deepak Agarwal, Anupama Muraleedharan:
Continuous Monitoring and Development of an Intelligent Tutoring System through Data Mining. T4E 2019: 252-253 - 2018
- [c64]Deepak Agarwal, Nishant Babel, Ryan S. Baker:
Contextual Derivation of Stable BKT Parameters for Analysing Content Efficacy. EDM 2018 - [c63]Deepak Agarwal, Kinjal Basu, Souvik Ghosh, Ying Xuan, Yang Yang, Liang Zhang:
Online Parameter Selection for Web-based Ranking Problems. KDD 2018: 23-32 - 2017
- [c62]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 - [c61]Tingting Cui, Lijun Peng, David Pardoe, Kun Liu, Deepak Agarwal, Deepak Kumar:
Data-Driven Reserve Prices for Social Advertising Auctions at LinkedIn. ADKDD@KDD 2017: 5:1-5:7 - [c60]Muthu Muthukrishnan, Andrew Tomkins, Larry P. Heck, Alborz Geramifard, Deepak Agarwal:
The Future of Artificially Intelligent Assistants. KDD 2017: 33-34 - [c59]Guangde Chen, Bee-Chung Chen, Deepak Agarwal:
Social Incentive Optimization in Online Social Networks. WSDM 2017: 547-556 - [i4]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) - 2016
- [c58]Deepak Agarwal:
Recommendations in the context of a Social Network. COMAD 2016 - [c57]Navneet Kapur, Nikita I. Lytkin, Bee-Chung Chen, Deepak Agarwal, Igor Perisic:
Ranking Universities Based on Career Outcomes of Graduates. KDD 2016: 137-144 - [c56]Liang Tang, Bo Long, Bee-Chung Chen, Deepak Agarwal:
An Empirical Study on Recommendation with Multiple Types of Feedback. KDD 2016: 283-292 - [c55]XianXing Zhang, Yitong Zhou, Yiming Ma, Bee-Chung Chen, Liang Zhang, Deepak Agarwal:
GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction. KDD 2016: 363-372 - [c54]Yang Yang, Liang Zhang, Ritesh Maheshwari, Zaid Ali Kahn, Deepak Agarwal, Sanjay Dubey:
Scout: A Point of Presence Recommendation System Using Real User Monitoring Data. PAM 2016: 205-217 - [c53]Xavier Amatriain, Deepak Agarwal:
Tutorial: Lessons Learned from Building Real-life Recommender Systems. RecSys 2016: 433 - 2015
- [c52]Deepak Agarwal:
Scaling Machine Learning and Statistics for Web Applications. KDD 2015: 1621 - [c51]Deepak Agarwal, Bee-Chung Chen, Qi He
Personalizing LinkedIn Feed. KDD 2015: 1651-1660 - [c50]Anurag Gupta, Deepak Agarwal, Derek Tan, Jakub Kulesza, Rahul Pathak, Stefano Stefani, Vidhya Srinivasan:
Amazon Redshift and the Case for Simpler Data Warehouses. SIGMOD Conference 2015: 1917-1923 - [c49]Deepak Agarwal, Shaunak Chatterjee, Yang Yang, Liang Zhang:
Constrained Optimization for Homepage Relevance. WWW (Companion Volume) 2015: 375-384 - 2014
- [j6]Dou Shen, Deepak Agarwal:
Introduction to the Special Issue on Online Advertising. ACM Trans. Intell. Syst. Technol. 5(4): 57:1-57:2 (2014) - [c48]Deepak Agarwal, Bee-Chung Chen, Rupesh Gupta, Joshua Hartman, Qi He
Activity ranking in LinkedIn feed. KDD 2014: 1603-1612 - [c47]Deepak Agarwal, Souvik Ghosh, Kai Wei, Siyu You:
Budget pacing for targeted online advertisements at LinkedIn. KDD 2014: 1613-1619 - [c46]Deepak Agarwal, Bo Long, Jonathan Traupman, Doris Xin, Liang Zhang:
LASER: a scalable response prediction platform for online advertising. WSDM 2014: 173-182 - 2013
- [j5]Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, Raghu Ramakrishnan:
Content recommendation on web portals. Commun. ACM 56(6): 92-101 (2013) - [j4]Deepak Agarwal, Rich Caruana, Jian Pei
Introduction to the Special Issue ACM SIGKDD 2012. ACM Trans. Knowl. Discov. Data 7(3): 9:1-9:2 (2013) - [c45]Rajiv Khanna, Liang Zhang, Deepak Agarwal, Bee-Chung Chen:
Parallel matrix factorization for binary response. IEEE BigData 2013: 430-438 - [c44]Deepak Agarwal:
Recommending items to users: an explore/exploit perspective. UEO@CIKM 2013: 1-2 - [c43]Deepak Agarwal:
Computational advertising: the linkedin way. CIKM 2013: 1585-1586 - [c42]Liang Tang, Rómer Rosales, Ajit Singh, Deepak Agarwal:
Automatic ad format selection via contextual bandits. CIKM 2013: 1587-1594 - [c41]Jaewon Yang, Bee-Chung Chen, Deepak Agarwal:
Estimating sharer reputation via social data calibration. KDD 2013: 59-67 - [c40]Cho-Jui Hsieh, Mitul Tiwari, Deepak Agarwal, Xinyi (Lisa) Huang, Sam Shah:
Organizational overlap on social networks and its applications. WWW 2013: 571-582 - 2012
- [c39]Deepak Agarwal, Bee-Chung Chen, Xuanhui Wang:
Multi-faceted ranking of news articles using post-read actions. CIKM 2012: 694-703 - [c38]Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, Xuanhui Wang:
Personalized click shaping through lagrangian duality for online recommendation. SIGIR 2012: 485-494 - [c37]Yandong Liu, Sandeep Pandey, Deepak Agarwal, Vanja Josifovski:
Finding the right consumer: optimizing for conversion in display advertising campaigns. WSDM 2012: 473-482 - [c36]Deepak Agarwal, Maxim Gurevich:
Fast top-k retrieval for model based recommendation. WSDM 2012: 483-492 - [c35]Deepak Agarwal, Sandeep Pandey, Vanja Josifovski:
Targeting converters for new campaigns through factor models. WWW 2012: 101-110 - [e1]Qiang Yang, Deepak Agarwal, Jian Pei:
The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, Beijing, China, August 12-16, 2012. ACM 2012, ISBN 978-1-4503-1462-6 [contents] - [i3]Rajiv Khanna, Liang Zhang, Deepak Agarwal, Bee-Chung Chen:
Parallel Matrix Factorization for Binary Response. CoRR abs/1203.5124 (2012) - [i2]Deepak Agarwal, Bee-Chung Chen, Xuanhui Wang:
Multi-Faceted Ranking of News Articles using Post-Read Actions. CoRR abs/1205.0591 (2012) - 2011
- [c34]Deepak Agarwal, Bee-Chung Chen, Bo Pang:
Personalized Recommendation of User Comments via Factor Models. EMNLP 2011: 571-582 - [c33]Yucheng Low, Deepak Agarwal, Alexander J. Smola:
Multiple domain user personalization. KDD 2011: 123-131 - [c32]Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, Xuanhui Wang:
Click shaping to optimize multiple objectives. KDD 2011: 132-140 - [c31]Aditya Krishna Menon, Krishna Prasad Chitrapura, Sachin Garg, Deepak Agarwal, Nagaraj Kota:
Response prediction using collaborative filtering with hierarchies and side-information. KDD 2011: 141-149 - [c30]Deepak Agarwal, Bee-Chung Chen, Bo Long:
Localized factor models for multi-context recommendation. KDD 2011: 609-617 - [c29]Nagaraj Kota, Deepak Agarwal:
Temporal multi-hierarchy smoothing for estimating rates of rare events. KDD 2011: 1361-1369 - [c28]Liang Zhang, Deepak Agarwal, Bee-Chung Chen:
Generalizing matrix factorization through flexible regression priors. RecSys 2011: 13-20 - [c27]Deepak Agarwal, Bee-Chung Chen:
Latent OLAP: data cubes over latent variables. SIGMOD Conference 2011: 877-888 - [c26]Deepak Agarwal:
Discussion of "Learning Scale Free Networks by Reweighted L1 regularization". AISTATS 2011: 49-50 - [c25]Deepak Agarwal, Lihong Li, Alexander J. Smola:
Linear-Time Estimators for Propensity Scores. AISTATS 2011: 93-100 - 2010
- [c24]Deepak Agarwal, Rahul Agrawal, Rajiv Khanna, Nagaraj Kota:
Estimating rates of rare events with multiple hierarchies through scalable log-linear models. KDD 2010: 213-222 - [c23]Deepak Agarwal, Bee-Chung Chen, Pradheep Elango:
Fast online learning through offline initialization for time-sensitive recommendation. KDD 2010: 703-712 - [c22]Deepak Agarwal, Datong Chen, Long-ji Lin, Jayavel Shanmugasundaram, Erik Vee:
Forecasting high-dimensional data. SIGMOD Conference 2010: 1003-1012 - [c21]Deepak Agarwal, Bee-Chung Chen:
fLDA: matrix factorization through latent dirichlet allocation. WSDM 2010: 91-100
2000 – 2009
- 2009
- [c20]Deepak Agarwal, Evgeniy Gabrilovich
Translating relevance scores to probabilities for contextual advertising. CIKM 2009: 1899-1902 - [c19]Yashar Moshfeghi
Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering. ECIR 2009: 54-65 - [c18]Deepak Agarwal, Bee-Chung Chen, Pradheep Elango:
Explore/Exploit Schemes for Web Content Optimization. ICDM 2009: 1-10 - [c17]Deepak Agarwal, Bee-Chung Chen:
Regression-based latent factor models. KDD 2009: 19-28 - [c16]Zhengdong Lu, Deepak Agarwal, Inderjit S. Dhillon:
A spatio-temporal approach to collaborative filtering. RecSys 2009: 13-20 - [c15]Deepak Agarwal, Bee-Chung Chen, Pradheep Elango:
Spatio-temporal models for estimating click-through rate. WWW 2009: 21-30 - 2008
- [c14]Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, Nitin Motgi, Seung-Taek Park, Raghu Ramakrishnan, Scott Roy, Joe Zachariah:
Online Models for Content Optimization. NIPS 2008: 17-24 - [c13]Liang Zhang, Deepak Agarwal:
Fast Computation of Posterior Mode in Multi-Level Hierarchical Models. NIPS 2008: 1913-1920 - [c12]Deepayan Chakrabarti
Contextual advertising by combining relevance with click feedback. WWW 2008: 417-426 - 2007
- [j3]Deepak Agarwal:
Detecting anomalies in cross-classified streams: a Bayesian approach. Knowl. Inf. Syst. 11(1): 29-44 (2007) - [c11]Dhiman Barman, Flip Korn, Divesh Srivastava, Dimitrios Gunopulos
Parsimonious Explanations of Change in Hierarchical Data. ICDE 2007: 1273-1275 - [c10]Sandeep Pandey, Deepayan Chakrabarti
Multi-armed bandit problems with dependent arms. ICML 2007: 721-728 - [c9]Neeta Nain, Vijay Laxmi, Manish Khandelwal, Deepak Agarwal, Dushyant Singh Rao, Pawan Kumar Verma:
Transformation Invariant Robust Shape Analysis. IPCV 2007: 275-281 - [c8]Deepak Agarwal, Dhiman Barman, Dimitrios Gunopulos
Efficient and effective explanation of change in hierarchical summaries. KDD 2007: 6-15 - [c7]Deepak Agarwal, Andrei Z. Broder, Deepayan Chakrabarti
Estimating rates of rare events at multiple resolutions. KDD 2007: 16-25 - [c6]Deepak Agarwal, Srujana Merugu:
Predictive discrete latent factor models for large scale dyadic data. KDD 2007: 26-35 - [c5]Sandeep Pandey, Deepak Agarwal, Deepayan Chakrabarti
Bandits for Taxonomies: A Model-based Approach. SDM 2007: 216-227 - 2006
- [j2]Li Wang, Matthew French, Azadeh Davoodi, Deepak Agarwal:
FPGA Dynamic Power Minimization through Placement and Routing Constraints. EURASIP J. Embed. Syst. 2006 (2006) - [c4]Deepak Agarwal, Andrew McGregor, Jeff M. Phillips, Suresh Venkatasubramanian, Zhengyuan Zhu
Spatial scan statistics: approximations and performance study. KDD 2006: 24-33 - [c3]Deepak Agarwal, Jeff M. Phillips, Suresh Venkatasubramanian:
The hunting of the bump: on maximizing statistical discrepancy. SODA 2006: 1137-1146 - 2005
- [j1]Shona Douglas, Deepak Agarwal, Tirso Alonso, Robert M. Bell, Mazin Gilbert, Deborah F. Swayne, Chris Volinsky:
Mining Customer Care Dialogs for "Daily News". IEEE Trans. Speech Audio Process. 13(5-1): 652-660 (2005) - [c2]Shawndra Hill, Deepak Agarwal, Robert M. Bell, Chris Volinsky:
Tuning representations of dynamic network data. LinkKDD 2005: 25-27 - [i1]Deepak Agarwal, Jeff M. Phillips, Suresh Venkatasubramanian:
The Hunting of the Bump: On Maximizing Statistical Discrepancy. CoRR abs/cs/0510004 (2005) - 2004
- [c1]Shona Douglas, Deepak Agarwal, Tirso Alonso, Robert M. Bell, Mazin G. Rahim, Deborah F. Swayne, Chris Volinsky:
Mining customer care dialogs for "daily news". INTERSPEECH 2004: 225-228
Coauthor Index
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last updated on 2024-09-21 01:42 CEST by the dblp team
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