BibSLEIGH — Deepak

Travelled to:

1 × China
1 × Greece
12 × USA
2 × Australia
2 × France

Collaborated with:

B.Chen N.Kota X.Wang P.Elango L.Zhang ∅ D.Chakrabarti S.Merugu R.Khanna J.Yang V.Josifovski B.Long Y.Low A.J.Smola Z.Lu I.S.Dhillon S.Pandey S.Ghosh K.Wei S.You R.Agrawal Y.Moshfeghi B.Piwowarski J.M.Jose A.K.Menon K.P.Chitrapura S.Garg D.Chen L.Lin J.Shanmugasundaram E.Vee E.Gabrilovich R.Hall A.McGregor J.M.Phillips S.Venkatasubramanian Z.Zhu Q.He Y.Ma P.Shivaswamy A.Z.Broder D.Diklic M.Sayyadian D.Barman D.Gunopulos N.E.Young F.Korn D.Srivastava A.Gupta D.Tan J.Kulesza R.Pathak S.Stefani V.Srinivasan Z.Hua G.Lebanon H.Tseng R.Gupta J.Hartman A.Iyer S.Kolar A.Singh

Talks about:

model (5) data (5) recommend (4) multipl (4) latent (4) factor (4) multi (4) estim (4) rate (4) hierarchi (3)

Person: Deepak Agarwal

DBLP: Agarwal:Deepak

Facilitated 1 volumes:

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2007

2006

Wrote 27 papers:

KDD-2015-Agarwal #machine learning #scalability #statistics #web: Scaling Machine Learning and Statistics for Web Applications (DA), p. 1621.
KDD-2015-AgarwalCHHLMSTY #personalisation: Personalizing LinkedIn Feed (DA, BCC, QH, ZH, GL, YM, PS, HPT, JY, LZ), pp. 1651–1660.
SIGMOD-2015-GuptaATKPSS: Amazon Redshift and the Case for Simpler Data Warehouses (AG, DA, DT, JK, RP, SS, VS), pp. 1917–1923.
KDD-2014-AgarwalCGHHIKMSSZ #process #ranking: Activity ranking in LinkedIn feed (DA, BCC, RG, JH, QH, AI, SK, YM, PS, AS, LZ), pp. 1603–1612.
KDD-2014-AgarwalGWY #online: Budget pacing for targeted online advertisements at LinkedIn (DA, SG, KW, SY), pp. 1613–1619.
KDD-2013-YangCA #social: Estimating sharer reputation via social data calibration (JY, BCC, DA), pp. 59–67.
CIKM-2012-AgarwalCW #multi #ranking #using: Multi-faceted ranking of news articles using post-read actions (DA, BCC, XW), pp. 694–703.
SIGIR-2012-AgarwalCEW #online #personalisation #recommendation: Personalized click shaping through lagrangian duality for online recommendation (DA, BCC, PE, XW), pp. 485–494.
KDD-2011-AgarwalCEW #multi: Click shaping to optimize multiple objectives (DA, BCC, PE, XW), pp. 132–140.
KDD-2011-AgarwalCL #locality #modelling #multi #recommendation: Localized factor models for multi-context recommendation (DA, BCC, BL), pp. 609–617.
KDD-2011-KotaA #multi: Temporal multi-hierarchy smoothing for estimating rates of rare events (NK, DA), pp. 1361–1369.
KDD-2011-LowAS #multi #personalisation: Multiple domain user personalization (YL, DA, AJS), pp. 123–131.
KDD-2011-MenonCGAK #collaboration #predict #using: Response prediction using collaborative filtering with hierarchies and side-information (AKM, KPC, SG, DA, NK), pp. 141–149.
RecSys-2011-ZhangAC #flexibility #matrix: Generalizing matrix factorization through flexible regression priors (LZ, DA, BCC), pp. 13–20.
SIGMOD-2011-AgarwalC: Latent OLAP: data cubes over latent variables (DA, BCC), pp. 877–888.
KDD-2010-AgarwalAKK #modelling #multi #scalability: Estimating rates of rare events with multiple hierarchies through scalable log-linear models (DA, RA, RK, NK), pp. 213–222.
KDD-2010-AgarwalCE #learning #online #performance #recommendation: Fast online learning through offline initialization for time-sensitive recommendation (DA, BCC, PE), pp. 703–712.
SIGMOD-2010-AgarwalCLSV: Forecasting high-dimensional data (DA, DC, LjL, JS, EV), pp. 1003–1012.
CIKM-2009-AgarwalGHJK: Translating relevance scores to probabilities for contextual advertising (DA, EG, RH, VJ, RK), pp. 1899–1902.
ECIR-2009-MoshfeghiAPJ #collaboration #predict #rating #recommendation #semantics: Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering (YM, DA, BP, JMJ), pp. 54–65.
KDD-2009-AgarwalC #modelling: Regression-based latent factor models (DA, BCC), pp. 19–28.
RecSys-2009-LuAD #approach #collaboration: A spatio-temporal approach to collaborative filtering (ZL, DA, ISD), pp. 13–20.
ICML-2007-PandeyCA #multi #problem: Multi-armed bandit problems with dependent arms (SP, DC, DA), pp. 721–728.
KDD-2007-AgarwalBCDJS #multi: Estimating rates of rare events at multiple resolutions (DA, AZB, DC, DD, VJ, MS), pp. 16–25.
KDD-2007-AgarwalBGYKS #effectiveness #performance #summary: Efficient and effective explanation of change in hierarchical summaries (DA, DB, DG, NEY, FK, DS), pp. 6–15.
KDD-2007-AgarwalM #modelling #predict #scalability: Predictive discrete latent factor models for large scale dyadic data (DA, SM), pp. 26–35.
KDD-2006-AgarwalMPVZ #approximate #performance #statistics: Spatial scan statistics: approximations and performance study (DA, AM, JMP, SV, ZZ), pp. 24–33.