Statistics > Machine Learning
arXiv:2205.00350 (stat)
[Submitted on 30 Apr 2022 (v1), last revised 20 Jun 2022 (this version, v2)]
Title:Orthogonal Statistical Learning with Self-Concordant Loss
View a PDF of the paper titled Orthogonal Statistical Learning with Self-Concordant Loss, by Lang Liu and 2 other authors
View PDFAbstract:Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component. We establish non-asymptotic bounds on the excess risk of orthogonal statistical learning methods with a loss function satisfying a self-concordance property. Our bounds improve upon existing bounds by a dimension factor while lifting the assumption of strong convexity. We illustrate the results with examples from multiple treatment effect estimation and generalized partially linear modeling.
| Comments: | COLT 2022 |
| Subjects: | Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG) |
| Cite as: | arXiv:2205.00350 [stat.ML] |
| (orarXiv:2205.00350v2 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2205.00350 arXiv-issued DOI via DataCite |
Submission history
From: Lang Liu [view email][v1] Sat, 30 Apr 2022 21:50:52 UTC (30 KB)
[v2] Mon, 20 Jun 2022 00:53:06 UTC (31 KB)
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View a PDF of the paper titled Orthogonal Statistical Learning with Self-Concordant Loss, by Lang Liu and 2 other authors
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