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
Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance
Lisha Chen, Heshan Fernando, Yiming Ying, Tianyi Chen
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability
Anass Aghbalou, Guillaume Staerman
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems
Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
Quantifying Overfitting: Evaluating Neural Network Performance through Analysis of Null Space
Hossein Rezaei, Mohammad Sabokrou
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization
Yufeng Zhang, Fengzhuo Zhang, Zhuoran Yang, Zhaoran Wang
Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data
Hongkuan Zhou, Zhenshan Bing, Xiangtong Yao, Xiaojie Su, Chenguang Yang, Kai Huang, Alois Knoll
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
Rui Yang, Yong Lin, Xiaoteng Ma, Hao Hu, Chongjie Zhang, Tong Zhang
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques
Daking Rai, Bailin Wang, Yilun Zhou, Ziyu Yao
Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-In
Zichun Yu, Chenyan Xiong, Shi Yu, Zhiyuan Liu
Compositional Generalization without Trees using Multiset Tagging and Latent Permutations
Matthias Lindemann, Alexander Koller, Ivan Titov
Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup
Damien Teney, Jindong Wang, Ehsan Abbasnejad
On the Generalization and Approximation Capacities of Neural Controlled Differential Equations
Linus Bleistein, Agathe Guilloux