Improving Generalization with Flat Hilbert Bayesian Inference
<|reference_start|>Improving Generalization with Flat Hilbert Bayesian Inference: We introduce Flat Hilbert Bayesian Inference (FHBI), an algorithm designed to enhance generalization in Bayesian inference. Our approach involves an iterative two-step procedure with an adversarial functional perturbation step and a functional descent step within the reproducing kernel Hilbert spaces. This methodology is supported by a theoretical analysis that extends previous findings on generalization ability from finite-dimensional Euclidean spaces to infinite-dimensional functional spaces. To evaluate the effectiveness of FHBI, we conduct comprehensive comparisons against seven baseline methods on the VTAB-1K benchmark, which encompasses 19 diverse datasets across various domains with diverse semantics. Empirical results demonstrate that FHBI consistently outperforms the baselines by notable margins, highlighting its practical efficacy.<|reference_end|>
@article{truong2024improving,
    title={Improving Generalization with Flat Hilbert Bayesian Inference},
    author={Tuan Truong, Quyen Tran, Quan Pham-Ngoc, Nhat Ho, Dinh Phung, Trung Le},
    journal={arXiv preprint arXiv:2410.04196},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04196},
    primaryClass={cs.LG stat.ML}
}