Strong Generalization
Strong generalization, the ability of machine learning models to perform well on unseen data, is a central objective in current research. Active areas of investigation include improving the robustness of self-supervised learning, understanding the optimization dynamics of transformers and other architectures (including CNNs and RNNs), and developing methods to enhance generalization through data augmentation, regularization techniques (e.g., logical regularization, consistency regularization), and improved training strategies (e.g., few-shot learning, meta-learning). These advancements are crucial for building reliable and adaptable AI systems across diverse applications, from image classification and natural language processing to healthcare and robotics.
Papers
Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies
Melody Wolk, Andy Applebaum, Camron Dennler, Patrick Dwyer, Marina Moskowitz, Harold Nguyen, Nicole Nichols, Nicole Park, Paul Rachwalski, Frank Rau, Adrian Webster
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes
Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine
Perturb Initial Features: Generalization of Neural Networks Under Sparse Features for Semi-supervised Node Classification
Yoonhyuk Choi, Jiho Choi, Taewook Ko, Chong-Kwon Kim
Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning
Ivaxi Sheth, Aamer Abdul Rahman, Mohammad Havaei, Samira Ebrahimi Kahou
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective
Yige Yuan, Bingbing Xu, Huawei Shen, Qi Cao, Keting Cen, Wen Zheng, Xueqi Cheng
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generalization in OOD Detection
Vahid Reza Khazaie, Anthony Wong, Mohammad Sabokrou