Work done at the SCALE-AI Chair at @polymtl.bsky.social with my fabulous co-authors Arthur Ferraz, Quentin Cappart, Axel Parmentier, Alexandre Forel, and Cheikh Ahmed... Happy #ORMS, #StrategicOptimization, and #MachineLearning, everyone!

Related links:
🔗Paper 1: arxiv.org/pdf/2402.06040
🔗Paper 2 (NeurIPS 2024): arxiv.org/pdf/2412.08287
🔗Source code: github.com/vidalt/Distr...

Stay tuned as we regularly share new discoveries on trustworthy #MachineLearning in connection with #GraphTheory, #ORMS, and Combinatorial Optimization. All the related papers are openly accessible, as well as the source codes:
github.com/vidalt
Thanks for following us! 🙌

This study was funded by SCALE-AI Canada through its "Research Chairs" program and Polytechnique Montréal (@polymtl.bsky.social). It has been a privilege to collaborate with the brilliant Julien Ferry, Ricardo Fukasawa, and Timothée Pascal on this!

Until February 14, 2025, you can vote for your favorite discovery on the list! If you would like to support our project, "L’intelligence artificielle : toujours confidentielle?", cast your vote here:
www.quebecscience.qc.ca/decouverte20...

Our work stood out for its critical focus on AI safety, with the jury emphasizing: "The rapid development of AI sometimes comes at the expense of public safety. Highlighting the risks of data non-confidentiality is a crucial step in establishing ethical guidelines."

All the source code and material to reproduce the experiments is available under an MIT license at github.com/alexforel/Ad.... Many thanks to my fabulous coauthors as well as SCALE-AI and @polymtl.bsky.social for the research support. Happy optimization, everyone... 😎

In a nutshell, the method works by iteratively refining and merging scenario sets to obtain tightened bounds. Convergence is guaranteed over a finite number of iterations, and we experimentally measure very significant speed-ups over direct solution approaches.

A bit late to this party... but can you add me? ;)