17 research outputs found
PAC-Bayesian Theory Meets Bayesian Inference
We exhibit a strong link between frequentist PAC-Bayesian risk bounds and the
Bayesian marginal likelihood. That is, for the negative log-likelihood loss
function, we show that the minimization of PAC-Bayesian generalization risk
bounds maximizes the Bayesian marginal likelihood. This provides an alternative
explanation to the Bayesian Occam's razor criteria, under the assumption that
the data is generated by an i.i.d distribution. Moreover, as the negative
log-likelihood is an unbounded loss function, we motivate and propose a
PAC-Bayesian theorem tailored for the sub-gamma loss family, and we show that
our approach is sound on classical Bayesian linear regression tasks.Comment: Published at NIPS 2015
(http://papers.nips.cc/paper/6569-pac-bayesian-theory-meets-bayesian-inference
PAC-Bayes-empirical-Bernstein inequality
We present PAC-Bayes-Empirical-Bernstein inequality. The inequality is based on combination of PAC-Bayesian bounding technique with Empirical Bernstein bound. It allows to take advantage of small empirical variance and is especially useful in regression. We show that when the empirical variance is significantly smaller than the empirical loss PAC-Bayes-Empirical-Bernstein inequality is significantly tighter than PAC-Bayes-kl inequality of Seeger (2002) and otherwise it is comparable. PAC-Bayes-Empirical-Bernstein inequality is an interesting example of application of PAC-Bayesian bounding technique to self-bounding functions. We provide empirical comparison of PAC-Bayes-Empirical-Bernstein inequality with PAC-Bayes-kl inequality on a synthetic example and several UCI datasets
PAC-Bayesian Inequalities for Martingales
We present a set of high-probability inequalities that control the
concentration of weighted averages of multiple (possibly uncountably many)
simultaneously evolving and interdependent martingales. Our results extend the
PAC-Bayesian analysis in learning theory from the i.i.d. setting to martingales
opening the way for its application to importance weighted sampling,
reinforcement learning, and other interactive learning domains, as well as many
other domains in probability theory and statistics, where martingales are
encountered.
We also present a comparison inequality that bounds the expectation of a
convex function of a martingale difference sequence shifted to the [0,1]
interval by the expectation of the same function of independent Bernoulli
variables. This inequality is applied to derive a tighter analog of
Hoeffding-Azuma's inequality
Online Clustering of Bandits
We introduce a novel algorithmic approach to content recommendation based on
adaptive clustering of exploration-exploitation ("bandit") strategies. We
provide a sharp regret analysis of this algorithm in a standard stochastic
noise setting, demonstrate its scalability properties, and prove its
effectiveness on a number of artificial and real-world datasets. Our
experiments show a significant increase in prediction performance over
state-of-the-art methods for bandit problems.Comment: In E. Xing and T. Jebara (Eds.), Proceedings of 31st International
Conference on Machine Learning, Journal of Machine Learning Research Workshop
and Conference Proceedings, Vol.32 (JMLR W&CP-32), Beijing, China, Jun.
21-26, 2014 (ICML 2014), Submitted by Shuai Li
(https://sites.google.com/site/shuailidotsli
A Primer on PAC-Bayesian Learning
International audienc
BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits
We propose a novel Bayesian-Optimistic Frequentist Upper Confidence Bound
(BOF-UCB) algorithm for stochastic contextual linear bandits in non-stationary
environments. This unique combination of Bayesian and frequentist principles
enhances adaptability and performance in dynamic settings. The BOF-UCB
algorithm utilizes sequential Bayesian updates to infer the posterior
distribution of the unknown regression parameter, and subsequently employs a
frequentist approach to compute the Upper Confidence Bound (UCB) by maximizing
the expected reward over the posterior distribution. We provide theoretical
guarantees of BOF-UCB's performance and demonstrate its effectiveness in
balancing exploration and exploitation on synthetic datasets and classical
control tasks in a reinforcement learning setting. Our results show that
BOF-UCB outperforms existing methods, making it a promising solution for
sequential decision-making in non-stationary environments