NeurIPS 2025 Workshop on Constrained Optimization for Machine Learning
Workshop at the Conference on Neural Information Processing Systems (NeurIPS) 2025
About the Workshop
As AI systems are increasingly deployed in safety-critical domains—including credit scoring, medical diagnosis, and autonomous systems—there is a growing demand to ensure their fairness, safety, robustness, and interpretability, alongside stronger calls for regulation. Constrained optimization offers an accountable framework for enforcing these requirements by embedding them directly into the training process, steering models to satisfy explicit constraints. This framework facilitates compliance with regulatory, industry, or ethical standards, which can be easily verified by checking constraint satisfaction.
This workshop explores constrained optimization as a principled method for enforcing desirable properties in machine learning models. It brings together experts in optimization, machine learning, and trustworthy AI to address the algorithmic and practical challenges of scaling constrained methods to modern deep learning settings, which are often large-scale, non-convex, and stochastic.
We invite contributions that advance the state of the art in Constrained Learning. For details, see the Call for Contributions.
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News
- We are accepting extended abstracts for oral and poster presentations. See the Call for Contributions for more details.
- If you are interested in reviewing for the workshop, please fill out the reviewer nomination form by August 21st.
- Submit questions for our panelists here.
Dates & Deadlines
The workshop will be held on Dec 6 or 7, 2025 in San Diego, California as part of NeurIPS 2025.- Extended abstract submission deadline: Aug 21, 2025 (AOE)
- Author notification: Sep 22, 2025
- NeurIPS early registration deadline: Oct 11, 2025 (AOE)
- Camera-ready version: Oct 31, 2025 (AOE)
- NeurIPS financial assistance application deadline: TBA
Speakers

Luiz Chamon
Hi! Paris & École Polytechnique

Frank E. Curtis
Lehigh University

Emily Ruth Diana
Carnegie Mellon University
