[UOS DS+] 해외연사 초청세미나 : 2025.12.12(금) 16:00~17:00
최고관리자
2025-12-05
[UOS DS+] 해외연사 초청세미나 : 2025.12.12(금) 16:00~17:00, 미래관 710호
- 신청 바로가기 : https://docs.google.com/forms/d/e/1FAIpQLScktgHOTT6C9NYav85ksMG0QR0Xj8S8jh2qcE1KTklLhFGAfw/viewform?usp=header
- 세미나 세부 안내
▶ Title : Some recent results on transfer learning
▶ Abstract : In the first part of the talk, I will introduce TRansfer leArning via guideD horseshoE prioR (TRADER), a novel approach enabling multi-source transfer through pre-trained models in high-dimensional linear regression. TRADER shrinks target parameters towards a weighted average of source estimates, accommodating sources with different scales. Theoretical investigation shows that TRADER achieves faster posterior contraction rates than standard continuous shrinkage priors when sources align well with the target while preventing negative transfer from heterogeneous sources. Extensive numerical studies and a real-data application demonstrate that TRADER improves estimation and inference accuracy over state-of-the-art transfer learning methods. In the second part of the talk, I will discuss some ongoing work involving transfer learning in nonparametric regression with ReLU networks.
▶ Speaker Bio : https://hernanmp.github.io/
- 신청 바로가기 : https://docs.google.com/forms/d/e/1FAIpQLScktgHOTT6C9NYav85ksMG0QR0Xj8S8jh2qcE1KTklLhFGAfw/viewform?usp=header
- 세미나 세부 안내
▶ Title : Some recent results on transfer learning
▶ Abstract : In the first part of the talk, I will introduce TRansfer leArning via guideD horseshoE prioR (TRADER), a novel approach enabling multi-source transfer through pre-trained models in high-dimensional linear regression. TRADER shrinks target parameters towards a weighted average of source estimates, accommodating sources with different scales. Theoretical investigation shows that TRADER achieves faster posterior contraction rates than standard continuous shrinkage priors when sources align well with the target while preventing negative transfer from heterogeneous sources. Extensive numerical studies and a real-data application demonstrate that TRADER improves estimation and inference accuracy over state-of-the-art transfer learning methods. In the second part of the talk, I will discuss some ongoing work involving transfer learning in nonparametric regression with ReLU networks.
▶ Speaker Bio : https://hernanmp.github.io/
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