第62回統計的機械学習セミナー / The 62nd Statistical Machine Learning Seminar
- 【日時】
- 2024年11月14日(木) 16:00 - 17:30
参加無料 / Admission Free
- 【場所】
- 統計数理研究所・D棟3階セミナー室4 (ハイブリッド)
オンライン参加を希望される場合は、以下の google form に登録し、Zoom情報をお受け取りください．
https://forms.gle/8miC8oN63TUjXXuK9
(現地参加の場合は登録不要です)
【Speaker】
Motonobu Kanagawa (EURECOM, France)
【Title】
Comparing Scale Parameter Estimators for Gaussian Process Regression: Cross Validation and Maximum Likelihood
【Abstract】
Gaussian process (GP) regression is a Bayesian nonparametric method for regression and interpolation, offering a principled way of quantifying the uncertainties of predicted function values. For the quantified uncertainties to be well-calibrated, however, the covariance kernel of the GP prior has to be carefully selected. In this work, we theoretically compare two methods for choosing the kernel in GP regression: cross-validation and maximum likelihood estimation. Focusing on the scale-parameter estimation of a Brownian motion kernel in the noiseless setting, we prove that cross-validation can yield asymptotically well-calibrated credible intervals for a broader class of ground-truth functions than maximum likelihood estimation, suggesting an advantage of the former over the latter.
【主催】
統計数理研究所 統計的機械学習研究センター/「統計的機械学習共創ラボ」プロジェクト
【連絡先】
福水健次
