Testing ADA on Synthetic and Real-World Data
15 Nov 2024
ADA boosts regression models' robustness through data augmentation, improving performance across real-world and synthetic data with varying hyperparameters.
How Hyperparameter Tuning Enhances Anchor Data Augmentation for Robust Regression
15 Nov 2024
Explore how hyperparameter tuning in ADA affects model performance, robustness, and optimization with a 1D cosine data example and minibatch augmentation.
ADA: A Powerful Data Augmentation Technique for Improved Regression Robustness
15 Nov 2024
ADA (Anchor Data Augmentation) offers a novel approach to data augmentation by mixing multiple samples based on clustering.
ADA's Impact on Out-of-Distribution Robustness
14 Nov 2024
This paper compares ADA's performance on out-of-distribution robustness tasks, highlighting its superiority with datasets like SkillCraft and RCFashionMNIST.
ADA Outperforms ERM and Competes with C-Mixup in In-Distribution Generalization Tasks
14 Nov 2024
This paper evaluates ADA's performance on in-distribution generalization tasks, comparing it to C-Mixup, Mixup, and other strategies.
ADA vs C-Mixup: Performance on California and Boston Housing Datasets
14 Nov 2024
This paper extends ADA’s evaluation to nonlinear regression on the California and Boston housing datasets, comparing its performance against C-Mixup.
Evaluating ADA: Experimental Results on Linear and Housing Datasets
14 Nov 2024
This paper presents experimental evaluations of ADA, comparing it with C-Mixup, vanilla augmentation, and classical risk minimization.
How to Implement ADA for Data Augmentation in Nonlinear Regression Models
14 Nov 2024
This paper presents the ADA algorithm for generating minibatches in nonlinear regression models, using stochastic gradient descent.
Anchor Data Augmentation as a Generalized Variant of C-Mixup
14 Nov 2024
ADA is a generalized version of C-Mixup that mixes multiple samples based on cluster membership, preserving nonlinear relationships in augmented regression data