aiqbench analysis
This research establishes a theoretical framework distinguishing between weak and strong transfer regimes in supervised learning, demonstrating how complementary information from source and target datasets can accelerate convergence rates. The authors introduce refined metrics for distributional relatedness that overcome limitations of previous unsupervised tools, offering a generic algorithmic approach that maintains near-optimality across all identified regimes without requiring prior knowledge of data distributions.
Help Me Help You: The Aggregate Value of Source and Target Data in Transfer Learning
arXiv:2408.16189v3 Announce Type: replace-cross Abstract: Transfer learning, in regression or classification, concerns problems where one aims to use imperfect data from a source distribution $P$ to improve prediction w.r.t. a target distribution $Q$. While there is now much theoretical understanding of the unsupervised setting of this problem (where no labeled target data is available), the supervised setting with labeled target data remains under-studied. We identify and characterize an interesting dichotomy in the rates achievable in the supervised setting: there is a weak transfer regime where optimal rates are determined by information in either the source or target dataset but not both, and a strong transfer regime where much faster target rates are achievable, accounting for complementary information in source and target data. Furthermore, while previous theoretical tools for the unsupervised setting can be extended to capture the weak transfer regime, we argue that they cannot capture the strong regime due to limitations inherent in the various notions of relatedness they rely on. Instead, we provide an appropriate refinement of relatedness notions---termed weak and strong moduli of transfer---that reveal the two regimes and lead to a nontrivial characterization of which pairs of distributions $P$ and $Q$ adhere to which regime. Finally, the above dichotomy reveals a new categorization of existing algorithmic approaches in terms of their optimality or lack-thereof across supervised regimes, and we provide a generic approach that remains nearly optimal in all regimes without prior distributional knowledge.
Abstract:Transfer learning, in regression or classification, concerns problems where one aims to use imperfect data from a source distribution $P$ to improve prediction w.r.t. a target distribution $Q$. While there is now much theoretical understanding of the unsupervised setting of this problem (where no labeled target data is available), the supervised setting with labeled target data remains under-studied. We identify and characterize an interesting dichotomy in the rates achievable in the supervised setting: there is a weak transfer regime where optimal rates are determined by information in either the source or target dataset but not both, and a strong transfer regime where much faster target rates are achievable, accounting for complementary information in source and target data. Furthermore, while previous theoretical tools for the unsupervised setting can be extended to capture the weak transfer regime, we argue that they cannot capture the strong regime due to limitations inherent in the various notions of relatedness they rely on. Instead, we provide an appropriate refinement of relatedness notions---termed weak and strong moduli of transfer---that reveal the two regimes and lead to a nontrivial characterization of which pairs of distributions $P$ and $Q$ adhere to which regime. Finally, the above dichotomy reveals a new categorization of existing algorithmic approaches in terms of their optimality or lack-thereof across supervised regimes, and we provide a generic approach that remains nearly optimal in all regimes without prior distributional knowledge.
This is a revision of a work previously posted to arXiv in 2024 by the same authors under the same arXiv number with a different title. The current version is revised to better highlight the key contributions. No AI was used in the production of this work
Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Statistics Theory (math.ST)
arXiv:2408.16189 [stat.ML]
(or arXiv:2408.16189v3 [stat.ML] for this version)
Full text under the publisher's open licence · 300 words
Related coverage
Stock Market Today Highlights, Sept 2: Sensex ends 373 pts lower at 76, 570, Nifty below 24, 000; Asian Paints, HDFC Bank fall most
thehindubusinessline.com · Sep 2, 2026
A Red Line and Oversight Framework for Government AI Contracts
AI Alignment Forum · Jul 18, 2026
State of the Field: AI for Epistemics and Coordination
LessWrong · Oct 2, 2026
Attention Decode on AMD MI450 GPUs: A Gluon Kernel Optimization Guide
AMD · Jul 27, 2026