Divergences, surrogate loss functions and experimental design XuanLong Nguyen University of California Berkeley, CA 94720

Source URL: dept.stat.lsa.umich.edu

• Divergence
• Statistical distance
• Distribution
• Constructible universe
• F-divergence
• Approximately finite-dimensional C*-algebra
• Symbol

CONCENTRATION OF THE MIXED DISCRIMINANT OF WELL-CONDITIONED MATRICES Alexander Barvinok Abstract. We call an n-tuple Q1 , . . . , Qn of positive definite n × n real matrices α-conditioned for some α ≥ 1 if for the c

Source URL: www.math.lsa.umich.edu

• Algebra
• Mathematics
• Linear algebra
• Matrices
• Matrix theory
• Quadratic forms
• Algebraic number theory
• Matrix
• Positive-definite matrix
• Symmetric matrix
• Nonnegative matrix
• Determinant

INTEGRATION AND OPTIMIZATION OF MULTIVARIATE POLYNOMIALS BY RESTRICTION ONTO A RANDOM SUBSPACE Alexander Barvinok February 2005

Source URL: www.math.lsa.umich.edu

• Polynomials
• Fourier analysis
• Group theory
• Vector space
• Vectors
• Holomorphic functional calculus
• Hermite polynomials

Estimating divergence functionals and the likelihood ratio by convex risk minimization XuanLong Nguyen Dept. of Statistical Science Duke University

Source URL: dept.stat.lsa.umich.edu

• Statistical distance
• Statistical theory
• KullbackLeibler divergence
• Thermodynamics
• Divergence
• F-divergence
• Estimation theory
• Hellinger distance

In-Network PCA and Anomaly Detection Ling Huang UC Berkeley XuanLong Nguyen

Source URL: dept.stat.lsa.umich.edu

• Algebra
• Mathematics
• Multivariate statistics
• Principal component analysis
• Intrusion detection system
• Eigenvalues and eigenvectors
• Anomaly detection
• Anomaly
• Dimensionality reduction

On divergences, surrogate loss functions, and decentralized detection XuanLong Nguyen Computer Science Division University of California, Berkeley

Source URL: dept.stat.lsa.umich.edu

• Mathematics
• Mathematical analysis
• Smooth functions
• Distribution
• Functional analysis
• Constructible universe
• Divergence
• Net
• operator
• Differential forms on a Riemann surface

Weiterbildung LSA-akkreditiert im Medienzentrum des Main-Taunus-Kreises (Kann Änderungen erfahren.) 32 Veranstaltungen 14 verschiedene Themen

Source URL: medienzentrum-main-taunus.de

AMALGAM ANOSOV REPRESENTATIONS RICHARD D. CANARY, MICHELLE LEE, AND MATTHEW STOVER, ´ SAMBARINO WITH AN APPENDIX BY THE AUTHORS AND ANDRES Abstract. Let Γ be a one-ended, torsion-free hyperbolic group and let G be a se

Source URL: www.math.lsa.umich.edu

Learning Conditional Latent Structures from Multiple Data Sources Viet Huynh1(B) , Dinh Phung1 , Long Nguyen2 , Svetha Venkatesh1 , and Hung H. Bui3 1

Source URL: dept.stat.lsa.umich.edu

• Statistics
• Probability
• Statistical natural language processing
• Probability distributions
• Stochastic processes
• Machine learning
• Statistical models
• Latent Dirichlet allocation
• Dirichlet process
• Dirichlet distribution
• Mixture model
• Gibbs sampling

THE COMPUTATIONAL COMPLEXITY OF CONVEX BODIES Alexander Barvinok and Ellen Veomett October 2006 Abstract. We discuss how well a given convex body B in a real d-dimensional vector space V can be approximated by a set X f

Source URL: www.math.lsa.umich.edu

• Geometry
• Mathematics
• Space
• Polytopes
• Operations research
• Convex analysis
• Real algebraic geometry
• Linear algebra
• Simplex
• Linear programming
• Mathematical optimization
• Convex hull