%0 Conference Paper %1 RSz18 %A Rivasplata, O. %A Szepesvári, Cs. %A Shawe-Taylor, J. %A Parrado-Hernandez, E. %A Sun, S. %B NIPS %D 2018 %K PAC-Bayes, bounds, generalization learning stability stability, theory, uniform %T PAC-Bayes bounds for stable algorithms with instance-dependent priors %X PAC-Bayes bounds have been proposed to get risk estimates based on a training sample. In this paper the PAC-Bayes approach is combined with stability of the hypothesis learned by a Hilbert space valued algorithm. The PAC-Bayes setting is used with a Gaussian prior centered at the expected output. Thus a novelty of our paper is using priors defined in terms of the data-generating distribution. Our main result estimates the risk of the randomized algorithm in terms of the hypothesis stability coefficients. We also provide a new bound for the SVM classifier, which is compared to other known bounds experimentally. Ours appears to be the first uniform hypothesis stability-based bound that evaluates to non-trivial values.

@inproceedings{RSz18, abstract = {PAC-Bayes bounds have been proposed to get risk estimates based on a training sample. In this paper the PAC-Bayes approach is combined with stability of the hypothesis learned by a Hilbert space valued algorithm. The PAC-Bayes setting is used with a Gaussian prior centered at the expected output. Thus a novelty of our paper is using priors defined in terms of the data-generating distribution. Our main result estimates the risk of the randomized algorithm in terms of the hypothesis stability coefficients. We also provide a new bound for the SVM classifier, which is compared to other known bounds experimentally. Ours appears to be the first uniform hypothesis stability-based bound that evaluates to non-trivial values.}, added-at = {2020-03-17T03:03:01.000+0100}, author = {Rivasplata, O. and {Sz}epesv{\'a}ri, {Cs}. and Shawe-Taylor, J. and Parrado-Hernandez, E. and Sun, S.}, bdsk-url-1 = {http://proceedings.mlr.press/v54/hanawal17a.html}, biburl = {https://www.bibsonomy.org/bibtex/2a5b4d83c2b6363da66f010842d30da5b/csaba}, booktitle = {NIPS}, date-added = {2018-11-02 00:02:30 +0000}, date-modified = {2018-11-02 00:17:27 +0000}, interhash = {c7a42db5823bf508bbe36e499b7de49b}, intrahash = {a5b4d83c2b6363da66f010842d30da5b}, keywords = {PAC-Bayes, bounds, generalization learning stability stability, theory, uniform}, month = {September}, pdf = {papers/NIPS2018-PACBayes.pdf}, timestamp = {2020-03-17T03:03:01.000+0100}, title = {{PAC-B}ayes bounds for stable algorithms with instance-dependent priors}, year = 2018 }