Search Results for author:
Found 7 papers, 2 papers with code
Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification
Graph Neural Networks (GNNs) have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques.
Generator Born from Classifier
In this paper, we make a bold attempt toward an ambitious task: given a pre-trained classifier, we aim to reconstruct an image generator, without relying on any data samples.
Robust Long-Tailed Learning via Label-Aware Bounded CVaR
Data in the real-world classification problems are always imbalanced or long-tailed, wherein the majority classes have the most of the samples that dominate the model training.
Distribution Shift Inversion for Out-of-Distribution Prediction
Machine learning society has witnessed the emergence of a myriad of Out-of-Distribution (OoD) algorithms, which address the distribution shift between the training and the testing distribution by searching for a unified predictor or invariant feature representation.
Slimmable Dataset Condensation
In this paper, we explore the problem of slimmable dataset condensation, to extract a smaller synthetic dataset given only previous condensation results.
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms
Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention.
