research
∙
07/17/2023
A General Framework for Learning under Corruption: Label Noise, Attribute Noise, and Beyond
Corruption is frequently observed in collected data and has been extensi...
share
research
∙
05/24/2023
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
Distribution shift (DS) may have two levels: the distribution itself cha...
research
∙
07/04/2022
Learning from Multiple Unlabeled Datasets with Partial Risk Regularization
Recent years have witnessed a great success of supervised deep learning,...
research
∙
04/07/2022
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients
Supervised federated learning (FL) enables multiple clients to share the...
research
∙
12/19/2021
Rethinking Importance Weighting for Transfer Learning
A key assumption in supervised learning is that training and test data f...
research
∙
02/01/2021
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
To cope with high annotation costs, training a classifier only from weak...
research
∙
10/05/2020
Pointwise Binary Classification with Pairwise Confidence Comparisons
Ordinary (pointwise) binary classification aims to learn a binary classi...
research
∙
07/08/2020
A One-step Approach to Covariate Shift Adaptation
A default assumption in many machine learning scenarios is that the trai...
research
∙
06/08/2020
Rethinking Importance Weighting for Deep Learning under Distribution Shift
Under distribution shift (DS) where the training data distribution diffe...
research
∙
10/20/2019
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach
From two unlabeled (U) datasets with different class priors, we can trai...
research
∙
08/31/2018