Just released @pyribs 0.11.0! Notable changes include the addition of Novelty Search with Local Competition and DDS-CNF by @efsiatras, and a bug fix by @zobarPk. As always, more details are available at https://t.co/ZnXLhVVq5e Cheers!
And that's a wrap! For a more detailed look into this release, see our What's New page: https://t.co/ZnXLhVVq5e And of course, reach out on GitHub, Twitter, or Discord (https://t.co/c1Hl7dtEkt) if you have any questions!
Excited to announce the release of @pyribs 0.10.0! Pyribs 0.10.0 introduces new algorithms (Discount Model Search and Dominated Novelty Search) and several features intended to make the library more flexible. Here are the highlights🧵
Furthermore, we have made a number of breaking changes to the CVTArchive, in particular so that it is easier to specify centroids and distance methods. We wrote a new tutorial that covers some of these changes: https://t.co/PGzCfirQNQ
Ecstatic to share our #ICLR2026 Oral paper, “Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces”, in collaboration with @xuster12 @tehqin17@snikolaidis19
Website: https://t.co/vty0PQMKMZ
Presentation @iclr_conf: https://t.co/gytXWqEyXX
Ecstatic to share our #ICLR2026 Oral paper, “Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces”, in collaboration with @xuster12 @tehqin17@snikolaidis19
Website: https://t.co/vty0PQMKMZ
Presentation @iclr_conf: https://t.co/gytXWqEyXX
Problems like the one above are specified by providing a dataset of images/measures. Given the ubiquity of datasets in machine learning, we are excited to see what problems can be framed in a similar way and tackled with DMS.
Happy to share that after a very hectic past couple of weeks, I have defended my thesis and am now #phdone! I am excited to start in my new role as a SWE @Waymo next year. Thank you to my committee and advisor @snikolaidis19 as well as my labmates @icaroslab, family, and friends!