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"abstract": "In this paper we analyze the graph-based approach to semi-supervised learning under a manifold assumption. We adopt a Bayesian perspective and demonstrate that, for a suitable choice of prior constructed with sufficiently many unlabeled data, the posterior contracts around the truth at a rate that is minimax optimal up to a logarithmic factor. Our theory covers both regression and classification.",
"authors": [
"Daniel Sanz-Alonso",
"Ruiyi Yang"
],
"emails": [
"sanzalonso@uchicago.edu",
"yry@uchicago.edu"
],
"id": "21-0084",
"issue": 97,
"pages": [
1,
28
],
"title": "Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective",