BibSLEIGH — Michael_I

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: Jordan:Michael_I=

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

1994

1993

1991

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.