Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator

ICLR 2019 • Makoto Yamada • Denny Wu • Yao-Hung Hubert Tsai • Ichiro Takeuchi • Ruslan Salakhutdinov • Kenji Fukumizu

Measuring divergence between two distributions is essential in machine learning and statistics and has various applications including binary classification, change point detection, and two-sample test. Furthermore, in the era of big data, designing divergence measure that is interpretable and can handle high-dimensional and complex data becomes extremely important... (read more)

PDF Abstract ICLR 2019 PDF ICLR 2019 Abstract

Code
Add Remove Mark official

No code implementations yet. Submit your code now

Tasks
Add Remove

Datasets

  Add Datasets introduced or used in this paper

Results from the Paper
Edit

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper
Add Remove

METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet
Contact us on: hello@paperswithcode.com. Papers With Code is a free resource with all data licensed under CC-BY-SA.
Terms Privacy Cookies policy