This paper compares ADA's performance on out-of-distribution robustness tasks, highlighting its superiority with datasets like SkillCraft and RCFashionMNIST.

This paper evaluates ADA's performance on in-distribution generalization tasks, comparing it to C-Mixup, Mixup, and other strategies.

This paper extends ADA’s evaluation to nonlinear regression on the California and Boston housing datasets, comparing its performance against C-Mixup.

This paper presents experimental evaluations of ADA, comparing it with C-Mixup, vanilla augmentation, and classical risk minimization.

This paper presents the ADA algorithm for generating minibatches in nonlinear regression models, using stochastic gradient descent.

ADA is a generalized version of C-Mixup that mixes multiple samples based on cluster membership, preserving nonlinear relationships in augmented regression data

Anchor Data Augmentation (ADA) is a domain-agnostic method for regression, using clustering to improve generalization with minimal computational cost.

Anchor Regression (AR) optimizes predictive accuracy while enhancing robustness to distribution shifts.

This paper explores various data augmentation methods, from human-designed domain-specific transformations to automated techniques using reinforcement learning.

This paper introduces Anchor Data Augmentation (ADA), a novel algorithm for enhancing nonlinear over-parameterized regression models.

ADA boosts regression models' robustness through data augmentation, improving performance across real-world and synthetic data with varying hyperparameters.

Explore how hyperparameter tuning in ADA affects model performance, robustness, and optimization with a 1D cosine data example and minibatch augmentation.

ADA (Anchor Data Augmentation) offers a novel approach to data augmentation by mixing multiple samples based on clustering.