Unbiased Estimation of the Eigenvalues of Large Implicit Matrices

Add to your list(s) Download to your calendar using vCal

• Professor Ryan Adams, Princeton
• Thursday 14 September 2017, 11:00-12:00
• CBL Room BE-438, Department of Engineering.

If you have a question about this talk, please contact Pat Wilson.

Many important problems are characterized by the eigenvalues of a large matrix. For example, the difficulty of many optimization problems, such as those arising from the fitting of large models in statistics and machine learning, can be investigated via the spectrum of the Hessian of the empirical loss function. Network data can be understood via the eigenstructure of the Laplacian matrix through spectral graph theory. Quantum simulations and other many-body problems are often characterized via the eigenvalues of the solution space, as are various dynamic systems. However, naive eigenvalue estimation is computationally expensive even when the matrix can be represented; in many of these situations the matrix is so large as to only be available implicitly via products with vectors. Even worse, one may only have noisy estimates of such matrix vector products. In this talk I will discuss how several different randomized techniques can be combined into a single procedure for unbiased estimates of the spectral density of large implicit matrices in the presence of noise.

This talk is part of the Machine Learning @ CUED series.

This talk is included in these lists:

• Seminar
• All Talks (aka the CURE list)
• Biology
• CBL Room BE-438, Department of Engineering
• CBL important
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge Neuroscience Seminars
• Cambridge talks
• Chris Davis' list
• Creating transparent intact animal organs for high-resolution 3D deep-tissue imaging
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• Joint Machine Learning Seminars
• Life Science
• Life Sciences
• ML
• Machine Learning @ CUED
• Machine Learning Summary
• Neuroscience
• Neuroscience Seminars
• Neuroscience Seminars
• Required lists for MLG
• Simon Baker's List
• Stem Cells & Regenerative Medicine
• Trust & Technology Initiative - interesting events
• bld31
• dh539
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.