Statistics > Machine Learning
arXiv:2408.15065 (stat)
[Submitted on 27 Aug 2024 (v1), last revised 11 Feb 2025 (this version, v2)]
Title:The Benefits of Balance: From Information Projections to Variance Reduction
View a PDF of the paper titled The Benefits of Balance: From Information Projections to Variance Reduction, by Lang Liu and 3 other authors
View PDFAbstract:Data balancing across multiple modalities and sources appears in various forms in foundation models in machine learning and AI, e.g. in CLIP and DINO. We show that data balancing across modalities and sources actually offers an unsuspected benefit: variance reduction. We present a non-asymptotic statistical bound that quantifies this variance reduction effect and relates it to the eigenvalue decay of Markov operators. Furthermore, we describe how various forms of data balancing in contrastive multimodal learning and self-supervised clustering can be better understood, and even improved upon, owing to our variance reduction viewpoint.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST) |
| Cite as: | arXiv:2408.15065 [stat.ML] |
| (orarXiv:2408.15065v2 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2408.15065 arXiv-issued DOI via DataCite |
Submission history
From: Ronak Mehta [view email][v1] Tue, 27 Aug 2024 13:48:15 UTC (4,663 KB)
[v2] Tue, 11 Feb 2025 17:47:11 UTC (1,378 KB)
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View a PDF of the paper titled The Benefits of Balance: From Information Projections to Variance Reduction, by Lang Liu and 3 other authors
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