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Michael I. Jordan
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#	Item
1	Dynamical, Symplectic and Stochastic Perspectives on Gradient-Based Optimization Michael I. Jordan University of California, Berkeley March 3, 2018 Add to Reading List Source URL: eta.impa.br Language: English - Date: 2018-07-28 17:09:47 Numerical analysis Dynamical systems Classical mechanics Hamiltonian mechanics Symplectic integrator Mathematical optimization Gradient descent Leapfrog integration Bregman Lagrangian Lagrangian mechanics Bregman divergence
2	Exploiting Tractable Substructures in Intractable Networks Lawrence K. Saul and Michael I. Jordan {lksaul.jordan}~psyche.mit.edu Center for Biological and Computational Learning Add to Reading List Source URL: papers.nips.cc - Date: 2014-04-14 17:08:18
3	Parallel Correlation Clustering on Big Graphs Xinghao Pan↵,✏ , Dimitris Papailiopoulos↵,✏ , Samet Oymak↵,✏ , Benjamin Recht↵,✏, , Kannan Ramchandran✏ , and Michael I. Jordan↵,✏, ↵ AMPLab, ✏ EECS Add to Reading List Source URL: papers.nips.cc - Date: 2016-02-16 15:47:47
4	Top 50 authors in computer science Author Michael I. Jordan Andrew Y. Ng Andrew Zisserman Add to Reading List Source URL: www.sciencemag.org Language: English - Date: 2016-04-20 10:52:25 Artificial intelligence Fellows of the Royal Society Ilya Sutskever Christos Faloutsos Andrew Zisserman Christos Michael I. Jordan Yoshua Bengio
5	Bioinformatics Advance Access published May 6, 2004 BIOINFORMATICS A statistical framework for genomic data fusion Gert R. G. Lanckriet 1, Tijl De Bie 2, Nello Cristianini 3, Michael I. Jordan 4 and William Stafford Nob Add to Reading List Source URL: eceweb.ucsd.edu Language: English - Date: 2015-07-31 19:00:24 Molecular biology Proteomics Statistical classification Geostatistics Kernel method Support vector machine Positive-definite kernel Kernel Membrane protein Gene expression Proteinprotein interaction Protein
6	A DIRECT FORMULATION FOR SPARSE PCA USING SEMIDEFINITE PROGRAMMING∗ ALEXANDRE D’ASPREMONT† , LAURENT EL GHAOUI‡ , MICHAEL I. JORDAN§ , AND GERT R. G. LANCKRIET¶ Abstract. Given a covariance matrix, we consider Add to Reading List Source URL: eceweb.ucsd.edu Language: English - Date: 2015-07-31 19:00:26 Mathematical optimization Matrix theory Multivariate statistics Linear programming Operations research Sparse PCA Semidefinite programming Principal component analysis Convex optimization Eigenvalues and eigenvectors Matrix Singular value decomposition
7	On the concentration of expectation and approximate inference in layered networks Michael I. Jordan University of California Berkeley, CA 94720 Add to Reading List Source URL: dept.stat.lsa.umich.edu Language: English - Date: 2004-01-18 19:14:54 Graphical models Probability distributions Bayesian network Networks Normal distribution Variational Bayesian methods Belief propagation Exponential family
8	Communication-Efficient Online Detection of Network-Wide Anomalies Ling Huang∗ XuanLong Nguyen∗ Minos Garofalakis† Joseph M. Hellerstein∗ Michael I. Jordan∗ Anthony D. Joseph∗ Nina Taft† ∗ UC Berkeley † Add to Reading List Source URL: dept.stat.lsa.umich.edu Language: English - Date: 2007-02-27 15:27:27 Statistics Algebra Error Hypothesis testing Design of experiments Multivariate statistics Measurement Principal component analysis Anomaly detection Eigenvalues and eigenvectors Type I and type II errors Errors and residuals
9	Covariance matrices for mean field variational Bayes Ryan Giordano, Tamara Broderick, Michael I. Jordan Berkeley Add to Reading List Source URL: www.tamarabroderick.com Language: English - Date: 2015-10-09 10:47:59
10	Posteriors, conjugacy, and exponential families for completely random measures Tamara Broderick, Ashia C. Wilson, Michael I. Jordan MIT Add to Reading List Source URL: www.tamarabroderick.com Language: English

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