2,451,418 research outputs found
Online Matrix Completion with Side Information
We give an online algorithm and prove novel mistake and regret bounds for
online binary matrix completion with side information. The mistake bounds we
prove are of the form . The term is
analogous to the usual margin term in SVM (perceptron) bounds. More
specifically, if we assume that there is some factorization of the underlying
matrix into where the rows of are interpreted
as "classifiers" in and the rows of as "instances" in
, then is the maximum (normalized) margin over all
factorizations consistent with the observed matrix. The
quasi-dimension term measures the quality of side information. In the
presence of vacuous side information, . However, if the side
information is predictive of the underlying factorization of the matrix, then
in an ideal case, where is the number of distinct row
factors and is the number of distinct column factors. We additionally
provide a generalization of our algorithm to the inductive setting. In this
setting, we provide an example where the side information is not directly
specified in advance. For this example, the quasi-dimension is now bounded
by
Random matrix techniques in quantum information theory
The purpose of this review article is to present some of the latest
developments using random techniques, and in particular, random matrix
techniques in quantum information theory. Our review is a blend of a rather
exhaustive review, combined with more detailed examples -- coming from research
projects in which the authors were involved. We focus on two main topics,
random quantum states and random quantum channels. We present results related
to entropic quantities, entanglement of typical states, entanglement
thresholds, the output set of quantum channels, and violations of the minimum
output entropy of random channels
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