Nozawa, K; Germain, P; Guedj, B; (2020) PAC-Bayesian Contrastive Unsupervised Representation Learning. In: Peters, Jonas and Sontag, David, (eds.) Proceedings of Machine Learning Research - Conference on Uncertainty in Artificial Intelligence. ML Research Press: Virtual.

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Abstract

Contrastive unsupervised representation learning (CURL) is the state-of-the-art technique to learn representations (as a set of features) from unlabelled data. While CURL has collected several empirical successes recently, theoretical understanding of its performance was still missing. In a recent work, Arora et al. (2019) provide the first generalisation bounds for CURL, relying on a Rademacher complexity. We extend their framework to the flexible PAC-Bayes setting, allowing to deal with the non-iid setting. We present PAC-Bayesian generalisation bounds for CURL, which are then used to derive a new representation learning algorithm. Numerical experiments on real-life datasets illustrate that our algorithm achieves competitive accuracy, and yields generalisation bounds with non-vacuous values.

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