MixMatch is a semi-supervised learning algorithm that unifies various dominant approaches, including entropy minimization, consistency regularization, and traditional regularization. It combines data augmentation, label guessing, and MixUp to process both labeled and unlabeled data. The algorithm generates augmented labeled examples and augmented unlabeled examples with guessed labels, which are then used to compute separate loss terms. MixMatch achieves state-of-the-art results on standard image benchmarks, reducing error rates by a significant margin, especially with few labeled examples. An ablation study reveals the importance of each component in MixMatch's success. Additionally, MixMatch demonstrates improved accuracy-privacy trade-offs in differential privacy settings, making it a powerful tool for semi-supervised learning and privacy-preserving machine learning.MixMatch is a semi-supervised learning algorithm that unifies various dominant approaches, including entropy minimization, consistency regularization, and traditional regularization. It combines data augmentation, label guessing, and MixUp to process both labeled and unlabeled data. The algorithm generates augmented labeled examples and augmented unlabeled examples with guessed labels, which are then used to compute separate loss terms. MixMatch achieves state-of-the-art results on standard image benchmarks, reducing error rates by a significant margin, especially with few labeled examples. An ablation study reveals the importance of each component in MixMatch's success. Additionally, MixMatch demonstrates improved accuracy-privacy trade-offs in differential privacy settings, making it a powerful tool for semi-supervised learning and privacy-preserving machine learning.