Person: Joydeep Ghosh

DBLP: Ghosh:Joydeep

Facilitated 1 volumes:

Ed

Contributed to:

2015

2014

2014

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2012

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1996

Wrote 25 papers:

KDD-2015-WangCGDKCMS #data analysis #health #named

Rubik: Knowledge Guided Tensor Factorization and Completion for Health Data Analytics (YW, RC, JG, JCD, ANK, YC, BAM, JS), pp. 1265–1274.

ICML-c2-2014-GunasekarRG #constraints #exponential #matrix #product line

Exponential Family Matrix Completion under Structural Constraints (SG, PR, JG), pp. 1917–1925.

KDD-2014-HoGS #health #named

Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization (JCH, JG, JS), pp. 115–124.

KDD-2014-ParkG #algorithm #health #named #rank #re-engineering

LUDIA: an aggregate-constrained low-rank reconstruction algorithm to leverage publicly released health data (YP, JG), pp. 55–64.

RecSys-2013-KoyejoAG #collaboration #matrix

Retargeted matrix factorization for collaborative filtering (OK, SA, JG), pp. 49–56.

RecSys-2012-RaghavanGG #collaboration #overview #quality

Review quality aware collaborative filtering (SR, SG, JG), pp. 123–130.

ICPR-2010-JunG #classification #process

Nearest-Manifold Classification with Gaussian Processes (GJ, JG), pp. 914–917.

KDIR-2010-Ghosh #locality #mining #modelling #multi #predict #scalability #using

Actionable Mining of Large, Multi-relational Data using Localized Predictive Models (JG), pp. 9–10.

KDIR-2010-JunGRO #image #predict #process

Predicting Ground-based Aerosol Optical Depth with Satellite Images Via Gaussian Processes (GJ, JG, VR, ZO), pp. 370–375.

ICML-2009-DeodharGGCD #clustering #framework #scalability #semistructured data

A scalable framework for discovering coherent co-clusters in noisy data (MD, GG, JG, HC, ISD), pp. 241–248.

KDD-2009-DaruruMWG #clustering #data flow #data mining #mining #parallel #pervasive #scalability

Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data (SD, NMM, MW, JG), pp. 1115–1124.

KDD-2009-DeodharG #mining #predict

Mining for the most certain predictions from dyadic data (MD, JG), pp. 249–258.

CIKM-2008-AcharyyaG #analysis #product line

A spam resistant family of concavo-convex ranks for link analysis (SA, JG), pp. 1505–1506.

KDD-2007-DeodharG #clustering #framework #learning

A framework for simultaneous co-clustering and learning from complex data (MD, JG), pp. 250–259.

ICML-2005-GuptaG #clustering #hybrid #robust #using

Robust one-class clustering using hybrid global and local search (GG, JG), pp. 273–280.

KDD-2005-BanerjeeKGBM #clustering #modelling

Model-based overlapping clustering (AB, CK, JG, SB, RJM), pp. 532–537.

KDD-2005-MeruguG #data flow #distributed #framework #learning #semistructured data

A distributed learning framework for heterogeneous data sources (SM, JG), pp. 208–217.

ICML-2004-BanerjeeDGM #analysis #estimation #exponential #product line

An information theoretic analysis of maximum likelihood mixture estimation for exponential families (AB, ISD, JG, SM).

KDD-2004-BanerjeeDGMM #approach #approximate #clustering #matrix

A generalized maximum entropy approach to bregman co-clustering and matrix approximation (AB, ISD, JG, SM, DSM), pp. 509–514.

KDD-2003-BanerjeeDGS #clustering #generative #modelling

Generative model-based clustering of directional data (AB, ISD, JG, SS), pp. 19–28.

KDD-2001-BasuMPG #using

Evaluating the novelty of text-mined rules using lexical knowledge (SB, RJM, KVP, JG), pp. 233–238.

ICML-1998-BollackerG #architecture #classification #reuse #scalability

A Supra-Classifier Architecture for Scalable Knowledge Reuse (KDB, JG), pp. 64–72.

ICPR-1996-BollackerG #linear

Linear feature extractors based on mutual information (KDB, JG), pp. 720–724.

ICPR-1996-RamamurtiG #adaptation

Structural adaptation in mixture of experts (VR, JG), pp. 704–708.

ICPR-1996-TumerG #classification #fault

Estimating the Bayes error rate through classifier combining (KT, JG), pp. 695–699.