Pairwise Learning

Pairwise learning focuses on comparing pairs of data points to learn relationships, rather than treating each point independently. Current research emphasizes developing efficient algorithms and model architectures, such as those based on contrastive learning, gradient alignment, and adaptive pairwise selection, to address the computational challenges inherent in pairwise comparisons, particularly in large datasets. This approach finds applications in diverse fields, including hyperparameter optimization, speaker verification, and recommender systems, improving model performance and robustness while addressing issues like fairness and scalability. The resulting advancements contribute to a deeper understanding of learning from relational data and enable more effective solutions in various machine learning tasks.