Statistical Learning
Learning via Wasserstein-Based High Probability Generalisation Bounds
The authors contributed equally to this work 37th Conference on Neural Information Processing Systems (NeurIPS 2023). Developing upper bounds on the generalisation gap, i.e., generalisation bounds has been a longstanding topic in statistical learning.
Learning via Wasserstein-Based High Probability Generalisation Bounds
The authors contributed equally to this work 37th Conference on Neural Information Processing Systems (NeurIPS 2023). Developing upper bounds on the generalisation gap, i.e., generalisation bounds has been a longstanding topic in statistical learning.
Diversifying Spatial-Temporal Perception for Video Domain Generalization Kun-Y u Lin
However, existing video classification models rely on the i.i.d.
Provable Guarantees for Neural Networks via Gradient Feature Learning
Neural networks have achieved remarkable empirical performance, while the current theoretical analysis is not adequate for understanding their success, e.g., the Neural Tangent Kernel approach fails to capture their key feature learning ability, while recent analyses on feature learning are typically problem-specific.
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
However, existing contrastive learning methods primarily focus on one single data level, which fails to fully exploit the intricate nature of medical time series.
