On the Rate of Convergence of Kolmogorov-Arnold Network Regression Estimators

A new framework has been developed that integrates sparse variational Gaussian process inference with Kolmogorov-Arnold Networks (KANs), enhancing their capability for uncertainty quantification in scientific machine learning applications. This approach allows for scalable Bayesian inference with reduced computational complexity, addressing a significant limitation of traditional methods.

The introduction of TabKAN, a novel framework for tabular data analysis utilizing Kolmogorov-Arnold Networks (KANs), addresses the challenges posed by heterogeneous feature types and missing values. This framework enhances interpretability and training efficiency through learnable activation functions on edges, marking a significant advancement in the field of machine learning.

The introduction of Softly Symbolified Kolmogorov-Arnold Networks (S2KAN) presents a significant advancement in interpretable machine learning by integrating symbolic primitives into the training process, allowing for more meaningful representations of data. This approach aims to enhance the symbolic fidelity of activations while maintaining the ability to fit complex data accurately.

The introduction of KAN-Dreamer integrates Kolmogorov-Arnold Networks (KANs) into the DreamerV3 framework, enhancing its function approximation capabilities. This development aims to improve sample efficiency in model-based reinforcement learning by replacing specific components with KAN and FastKAN layers, while ensuring computational efficiency through a fully vectorized implementation in the JAX-based World Model.