ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2310.10611
  4. Cited By
IW-GAE: Importance Weighted Group Accuracy Estimation for Improved
  Calibration and Model Selection in Unsupervised Domain Adaptation
v1v2 (latest)

IW-GAE: Importance Weighted Group Accuracy Estimation for Improved Calibration and Model Selection in Unsupervised Domain Adaptation

International Conference on Machine Learning (ICML), 2023
16 October 2023
Taejong Joo
Diego Klabjan
ArXiv (abs)PDFHTML

Papers citing "IW-GAE: Importance Weighted Group Accuracy Estimation for Improved Calibration and Model Selection in Unsupervised Domain Adaptation"

1 / 1 papers shown
Title
Improving self-training under distribution shifts via anchored
  confidence with theoretical guarantees
Improving self-training under distribution shifts via anchored confidence with theoretical guaranteesNeural Information Processing Systems (NeurIPS), 2024
Taejong Joo
Diego Klabjan
UQCV
202
0
0
01 Nov 2024
1