<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0">
  <channel>
    <title>PyPI recent updates for bayesian-gp-cvloss</title>
    <link>https://pypi.org/project/bayesian-gp-cvloss/</link>
    <description>Recent updates to the Python Package Index for bayesian-gp-cvloss</description>
    <language>en</language>    <item>
      <title>0.2.0</title>
      <link>https://pypi.org/project/bayesian-gp-cvloss/0.2.0/</link>
      <description>A Python package for Gaussian Process Regression with hyperparameter optimization using Hyperopt and cross-validation, focusing on optimizing cross-validated loss.</description>
<author>sfzhong@tongji.edu.cn</author>      <pubDate>Tue, 07 Apr 2026 10:04:57 GMT</pubDate>
    </item>    <item>
      <title>0.1.7</title>
      <link>https://pypi.org/project/bayesian-gp-cvloss/0.1.7/</link>
      <description>A Python package for Gaussian Process Regression with hyperparameter optimization using Hyperopt and cross-validation, focusing on optimizing cross-validated loss.</description>
<author>sfzhong@tongji.edu.cn</author>      <pubDate>Mon, 16 Mar 2026 15:34:38 GMT</pubDate>
    </item>    <item>
      <title>0.1.6</title>
      <link>https://pypi.org/project/bayesian-gp-cvloss/0.1.6/</link>
      <description>A Python package for Gaussian Process Regression with hyperparameter optimization using Hyperopt and cross-validation, focusing on optimizing cross-validated loss.</description>
<author>sfzhong@tongji.edu.cn</author>      <pubDate>Mon, 16 Mar 2026 15:22:05 GMT</pubDate>
    </item>    <item>
      <title>0.1.5</title>
      <link>https://pypi.org/project/bayesian-gp-cvloss/0.1.5/</link>
      <description>A Python package for Gaussian Process Regression with hyperparameter optimization using Hyperopt and cross-validation, focusing on optimizing cross-validated loss.</description>
<author>sfzhong@tongji.edu.cn</author>      <pubDate>Tue, 27 May 2025 05:01:20 GMT</pubDate>
    </item>    <item>
      <title>0.1.4</title>
      <link>https://pypi.org/project/bayesian-gp-cvloss/0.1.4/</link>
      <description>A Python package for Gaussian Process Regression with hyperparameter optimization using Hyperopt and cross-validation, focusing on optimizing cross-validated loss.</description>
<author>sfzhong@tongji.edu.cn</author>      <pubDate>Tue, 27 May 2025 04:25:16 GMT</pubDate>
    </item>    <item>
      <title>0.1.3</title>
      <link>https://pypi.org/project/bayesian-gp-cvloss/0.1.3/</link>
      <description>A Python package for Gaussian Process Regression with hyperparameter optimization using Hyperopt and cross-validation, focusing on optimizing cross-validated loss.</description>
<author>sfzhong@tongji.edu.cn</author>      <pubDate>Tue, 27 May 2025 04:20:00 GMT</pubDate>
    </item>    <item>
      <title>0.1.2</title>
      <link>https://pypi.org/project/bayesian-gp-cvloss/0.1.2/</link>
      <description>A Python package for Gaussian Process Regression with hyperparameter optimization using Hyperopt and cross-validation, focusing on optimizing cross-validated loss.</description>
<author>sfzhong@tongji.edu.cn</author>      <pubDate>Mon, 26 May 2025 09:31:20 GMT</pubDate>
    </item>    <item>
      <title>0.1.1</title>
      <link>https://pypi.org/project/bayesian-gp-cvloss/0.1.1/</link>
      <description>A Python package for Gaussian Process Regression with hyperparameter optimization using Hyperopt and cross-validation, focusing on optimizing cross-validated loss.</description>
<author>sfzhong@tongji.edu.cn</author>      <pubDate>Mon, 26 May 2025 09:26:02 GMT</pubDate>
    </item>  </channel>
</rss>