Written by Emtiyaz, CS, UBC.
Last updated: Nov. 15, 2011.
Update on May 1, 2012: Matt Hoffman wrote a version of the code in Python [ Download ]
Description: This matlab code can be used to compute an upper bound to expectation of log(1+exp(x)) with respect to a Gaussian disctribution.
These bounds can be used to model binary data as in Bayesian logistic
regression, binary Gaussian Process Classification, and binary factor
analysis. See our ICML-2011 paper for
details.
Download: piecewiseBounds.zip
How to use the code:
This piece of code computes an upper bound to the expectation of log(1+exp(x))) with respect to Gaussian with mean m and variance v.
> bound = getBound(’linear’,20); % get linear bound with 20 pieces
> m = [1 0 -1]’; v = [ 1 2 1]’; % specify mean and variance
> [f,gm,gv] = funObj_pw(m,v,bound); % compute upper bound and gradient wrt m and v
f is 3x1 vector containing upper bounds to the expectation with respect to
Gaussian with mean m and variance v, and [gm,gv] are the corresponding
gradients.
Description of files:
- funObj_pw.m computes the upper bound and gradients.
- objgradpart_vec.m computes truncated
Gaussian moments as described in the appendix.
- getPiecewiseBound.m, getPieceWiseLinear.m and battlse.m are used to compute piecewise bounds.
System requirements and dependencies: The code works fine on
MATLAB 7.4 (2007a) and higher versions. Function to compute linear piecewise
bounds is written by Hsiung, Kim and Boyd.
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