research
∙
05/27/2023
Toward Understanding Generative Data Augmentation
Generative data augmentation, which scales datasets by obtaining fake la...
share
research
∙
05/09/2023
Towards Understanding Generalization of Macro-AUC in Multi-label Learning
Macro-AUC is the arithmetic mean of the class-wise AUCs in multi-label l...
research
∙
02/05/2023
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
A large-scale deep model pre-trained on massive labeled or unlabeled dat...
research
∙
04/30/2022
Deep Ensemble as a Gaussian Process Approximate Posterior
Deep Ensemble (DE) is an effective alternative to Bayesian neural networ...
research
∙
07/21/2021
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
Zeroth-order (ZO) optimization is widely used to handle challenging task...
research
∙
06/08/2021
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
Recently, the (gradient-based) bilevel programming framework is widely u...
research
∙
05/10/2021
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization
(Partial) ranking loss is a commonly used evaluation measure for multi-l...
research
∙
11/16/2020
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?
Various evaluation measures have been developed for multi-label classifi...
research
∙
11/05/2019
