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Optimization for ML and AI Seminar: Self-play Algorithms for Math Theorem Proving

HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

Tengyu Ma, Stanford University Abstract: I will discuss RL algorithms for automated theorem proving with LLMs, especially in the possible future regime where we run out of high-quality training data. To keep improving the models with limited data, we draw inspiration from mathematicians, who continuously develop new results, partly by proposing novel conjectures or exercises […]

Optimization for ML and AI Seminar: A non-equilibrium phase transition with broken ergodicity leads to double descent and benign overfitting in machine learning

HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

Nigel Goldenfeld, UC San Diego Department of Physics and HDSI Abstract: The remarkable ability of modern neural networks to generalize improves with increasing network capacity, even when the number of model parameters or effective degrees of freedom exceeds the number of training data points. This phenomenon is all the more surprising given that generalization error […]

CVPR 2026 Workshop: Trustworthy, Robust, Uncertainty-Aware, and Explainable Visual Intelligence and Beyond (TRUE-V)

IEEE/CVF Conference on Computer Vision and Pattern Recognition Denver, CO, United States

Contemporary vision models and vision–language models are increasingly deployed in high-stakes domains, yet remain opaque, fragile, and difficult to align across tasks and modalities. This workshop aim to foster dialogue on the urgent need for transparent, reliable, and safe computer vision systems, especially in critical domains such as healthcare, transportation, and legal decision making. It brings together […]