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Travelled to:
1 × China
1 × Finland
1 × Germany
1 × Israel
1 × United Kingdom
15 × USA
2 × France
3 × Australia
3 × Canada
Collaborated with:
P.Liang D.M.Blei L.Huang A.X.Zheng E.P.Xing B.Kulis F.R.Bach A.Y.Ng P.Sarkar M.J.Wainwright F.L.Wauthier N.Jojic J.G.Dy L.W.Mackey D.Klein B.Liblit A.Kleiner A.Talwalkar G.R.G.Lanckriet R.A.Jacobs D.E.Rumelhart A.Aiken W.Xu A.Fox D.A.Patterson M.Naik Y.Zhang T.Broderick J.W.Paisley D.Chakrabarti Y.Guan J.C.Duchi D.J.Weiss D.Niu D.Ting D.Yan B.Taskar J.Nilsson F.Sha B.E.Engelhardt S.E.Brenner X.Nguyen R.Sharan F.D.Bernardinis A.L.Sangiovanni-Vincentelli R.M.Karp S.P.Singh T.S.Jaakkola B.Mozafari S.Madden S.Agarwal I.Stoica E.B.Fox E.B.Sudderth A.S.Willsky K.Sohn Y.W.Teh R.Nishihara L.Lessard B.Recht A.Packard J.Schulman S.Levine P.Abbeel P.Moritz M.J.Franklin A.Aiken N.Cristianini P.L.Bartlett L.E.Ghaoui C.Ma V.Smith M.Jaggi P.Richtárik M.Takác C.Ré D.Agrawal M.Balazinska M.I.Cafarella T.Kraska R.Ramakrishnan H.Milner
Talks about:
learn (9) bayesian (6) model (6) algorithm (5) statist (5) process (5) kernel (5) data (5) analysi (4) system (4)

Person: Michael I. Jordan

DBLP DBLP: Jordan:Michael_I=

Contributed to:

ICML 20152015
PODS 20152015
SIGMOD 20152015
VLDB 20152014
SIGMOD 20142014
ICML c3 20132013
KDD 20132013
ICML 20122012
KDD 20122012
ICML 20112011
ICML 20102010
ICML 20092009
KDD 20092009
SOSP 20092009
ICML 20082008
ICML 20072007
ICML 20062006
ICML 20052005
PLDI 20052005
ICML 20042004
DAC 20032003
PLDI 20032003
SIGIR 20032003
ICML 20022002
ICML 20012001
SIGIR 20012001
ICML 19941994
ICML 19931993
ML 19911991

Wrote 51 papers:

ICML-2015-MaSJJRT #distributed #optimisation
Adding vs. Averaging in Distributed Primal-Dual Optimization (CM, VS, MJ, MIJ, PR, MT), pp. 1973–1982.
ICML-2015-NishiharaLRPJ #analysis #convergence
A General Analysis of the Convergence of ADMM (RN, LL, BR, AP, MIJ), pp. 343–352.
ICML-2015-SchulmanLAJM #optimisation #policy #trust
Trust Region Policy Optimization (JS, SL, PA, MIJ, PM), pp. 1889–1897.
ICML-2015-ZhangWJ #algorithm #bound #distributed #estimation #matrix #performance #rank
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds (YZ, MJW, MIJ), pp. 457–465.
PODS-2015-Jordan #big data
Computational Thinking, Inferential Thinking and “Big Data” (MIJ), p. 1.
SIGMOD-2015-ReABCJKR #database #machine learning #question
Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype? (CR, DA, MB, MIC, MIJ, TK, RR), pp. 283–284.
VLDB-2015-MozafariSFJM14 #dataset #learning #scalability
Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning (BM, PS, MJF, MIJ, SM), pp. 125–136.
SIGMOD-2014-AgarwalMKTJMMS #approximate #performance #query #reliability
Knowing when you’re wrong: building fast and reliable approximate query processing systems (SA, HM, AK, AT, MIJ, SM, BM, IS), pp. 481–492.
ICML-c3-2013-BroderickKJ #named
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes (TB, BK, MIJ), pp. 226–234.
ICML-c3-2013-WauthierJJ #performance #ranking
Efficient Ranking from Pairwise Comparisons (FLW, MIJ, NJ), pp. 109–117.
KDD-2013-KleinerTASJ #performance
A general bootstrap performance diagnostic (AK, AT, SA, IS, MIJ), pp. 419–427.
ICML-2012-KleinerTSJ #big data
The Big Data Bootstrap (AK, AT, PS, MIJ), p. 232.
ICML-2012-KulisJ #algorithm
Revisiting k-means: New Algorithms via Bayesian Nonparametrics (BK, MIJ), p. 148.
ICML-2012-PaisleyBJ #probability
Variational Bayesian Inference with Stochastic Search (JWP, DMB, MIJ), p. 177.
ICML-2012-SarkarCJ #network #parametricity #predict
Nonparametric Link Prediction in Dynamic Networks (PS, DC, MIJ), p. 246.
KDD-2012-Jordan #big data #divide and conquer #statistics
Divide-and-conquer and statistical inference for big data (MIJ), p. 4.
KDD-2012-WauthierJJ #clustering #nondeterminism #reduction
Active spectral clustering via iterative uncertainty reduction (FLW, NJ, MIJ), pp. 1339–1347.
ICML-2011-GuanDJ #feature model #probability
A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection (YG, JGD, MIJ), pp. 1073–1080.
ICML-2010-DuchiMJ #algorithm #consistency #on the #ranking
On the Consistency of Ranking Algorithms (JCD, LWM, MIJ), pp. 327–334.
ICML-2010-LiangJK #approach #learning #source code
Learning Programs: A Hierarchical Bayesian Approach (PL, MIJ, DK), pp. 639–646.
ICML-2010-MackeyWJ #matrix
Mixed Membership Matrix Factorization (LWM, DJW, MIJ), pp. 711–718.
ICML-2010-NiuDJ #clustering #multi
Multiple Non-Redundant Spectral Clustering Views (DN, JGD, MIJ), pp. 831–838.
ICML-2010-TingHJ #analysis #convergence #graph
An Analysis of the Convergence of Graph Laplacians (DT, LH, MIJ), pp. 1079–1086.
ICML-2010-XuHFPJ #detection #mining #problem #scalability
Detecting Large-Scale System Problems by Mining Console Logs (WX, LH, AF, DAP, MIJ), pp. 37–46.
ICML-2009-LiangJK #exponential #learning #metric #product line
Learning from measurements in exponential families (PL, MIJ, DK), pp. 641–648.
KDD-2009-YanHJ #approximate #clustering #performance
Fast approximate spectral clustering (DY, LH, MIJ), pp. 907–916.
SOSP-2009-XuHFPJ #detection #mining #problem #scalability
Detecting large-scale system problems by mining console logs (WX, LH, AF, DAP, MIJ), pp. 117–132.
ICML-2008-FoxSJW #persistent
An HDP-HMM for systems with state persistence (EBF, EBS, MIJ, ASW), pp. 312–319.
ICML-2008-LiangJ #analysis #generative #pseudo
An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators (PL, MIJ), pp. 584–591.
ICML-2007-LiangJT #modelling
A permutation-augmented sampler for DP mixture models (PL, MIJ, BT), pp. 545–552.
ICML-2007-NilssonSJ #kernel #reduction #using
Regression on manifolds using kernel dimension reduction (JN, FS, MIJ), pp. 697–704.
ICML-2006-EngelhardtJB #predict #visual notation
A graphical model for predicting protein molecular function (BEE, MIJ, SEB), pp. 297–304.
ICML-2006-XingSJT #multi #process #type inference
Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture (EPX, KAS, MIJ, YWT), pp. 1049–1056.
ICML-2006-ZhengJLNA #debugging #identification #multi #statistics
Statistical debugging: simultaneous identification of multiple bugs (AXZ, MIJ, BL, MN, AA), pp. 1105–1112.
ICML-2005-BachJ #composition #kernel #predict #rank
Predictive low-rank decomposition for kernel methods (FRB, MIJ), pp. 33–40.
PLDI-2005-LiblitNZAJ #debugging #scalability #statistics
Scalable statistical bug isolation (BL, MN, AXZ, AA, MIJ), pp. 15–26.
ICML-2004-BachLJ #algorithm #kernel #learning #multi
Multiple kernel learning, conic duality, and the SMO algorithm (FRB, GRGL, MIJ).
ICML-2004-BleiJ #process
Variational methods for the Dirichlet process (DMB, MIJ).
ICML-2004-NguyenWJ #classification #detection #distributed #kernel #using
Decentralized detection and classification using kernel methods (XN, MJW, MIJ).
ICML-2004-XingSJ #process #type inference
Bayesian haplo-type inference via the dirichlet process (EPX, RS, MIJ).
DAC-2003-BernardinisJS #performance #representation
Support vector machines for analog circuit performance representation (FDB, MIJ, ALSV), pp. 964–969.
PLDI-2003-LiblitAZJ #debugging
Bug isolation via remote program sampling (BL, AA, AXZ, MIJ), pp. 141–154.
SIGIR-2003-BleiJ #modelling
Modeling annotated data (DMB, MIJ), pp. 127–134.
ICML-2002-LanckrietCBGJ #kernel #learning #matrix #programming
Learning the Kernel Matrix with Semi-Definite Programming (GRGL, NC, PLB, LEG, MIJ), pp. 323–330.
ICML-2001-NgJ #classification #convergence #feature model
Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection (AYN, MIJ), pp. 377–384.
ICML-2001-XingJK #array #feature model
Feature selection for high-dimensional genomic microarray data (EPX, MIJ, RMK), pp. 601–608.
SIGIR-2001-ZhengNJ #algorithm #analysis
Stable Algorithms for Link Analysis (AXZ, AYN, MIJ), pp. 258–266.
ICML-1994-Jordan #approach #modelling #statistics
A Statistical Approach to Decision Tree Modeling (MIJ), pp. 363–370.
ICML-1994-SinghJJ #learning #markov #process
Learning Without State-Estimation in Partially Observable Markovian Decision Processes (SPS, TSJ, MIJ), pp. 284–292.
ICML-1993-JordanJ #approach #divide and conquer #learning #statistics
Supervised Learning and Divide-and-Conquer: A Statistical Approach (MIJ, RAJ), pp. 159–166.
ML-1991-JordanR #learning #modelling
Internal World Models and Supervised Learning (MIJ, DER), pp. 70–74.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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