Visual reinforcement learning (RL), which makes decisions directly from high-dimensional
visual inputs, has demonstrated significant potential in various domains. However …

Generative data augmentation, which scales datasets by obtaining fake labeled examples
from a trained conditional generative model, boosts classification performance in various …

Generative data augmentation (GDA) has emerged as a promising technique to alleviate
data scarcity in machine learning applications. This thesis presents a comprehensive survey …

Modern deep learning models with great expressive power can be trained to overfit the
training data but still generalize well. This phenomenon is referred to as benign overfitting …

The implicit bias towards solutions with favorable properties is believed to be a key reason
why neural networks trained by gradient-based optimization can generalize well. While the …

Convolutional neural networks (CNNs) have achieved impressive results on imbalanced
image data, but they still have difficulty generalizing to minority classes and their decisions …

A common explanation for the failure of out-of-distribution (OOD) generalization is that the
model trained with empirical risk minimization (ERM) learns spurious features instead of …

The challenge of overfitting, in which the model memorizes the training data and fails to
generalize to test data, has become increasingly significant in the training of large neural …

We study the problem of (learning) algorithm comparison, where the goal is to find
differences between models trained with two different learning algorithms. We begin by …

Mixup, a simple data augmentation method that randomly mixes two data points via linear
interpolation, has been extensively applied in various deep learning applications to gain …