ICML 2016poster10 citations
Black-box Optimization with a Politician
Abstract
We propose a new framework for black-box convex optimization which is well-suited for situations where gradient computations are expensive. We derive a new method for this framework which leverages several concepts from convex optimization, from standard first-order methods (e.g. gradient descent or quasi-Newton methods) to analytical centers (i.e. minimizers of self-concordant barriers). We demonstrate empirically that our new technique compares favorably with state of the art algorithms (such as BFGS).
BibTeX
@InProceedings{pmlr-v48-bubeck16,
title = {Black-box Optimization with a Politician},
author = {Bubeck, Sebastien and Lee, Yin Tat},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {1624--1631},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/bubeck16.pdf},
url = {https://proceedings.mlr.press/v48/bubeck16.html},
abstract = {We propose a new framework for black-box convex optimization which is well-suited for situations where gradient computations are expensive. We derive a new method for this framework which leverages several concepts from convex optimization, from standard first-order methods (e.g. gradient descent or quasi-Newton methods) to analytical centers (i.e. minimizers of self-concordant barriers). We demonstrate empirically that our new technique compares favorably with state of the art algorithms (such as BFGS).}
}