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Optimization for ML and AI Seminar: A survey of the mixing times of the Proximal Sampler algorithm

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

Andre Wibisono, Yale University Abstract: Sampling is a fundamental algorithmic task with many connections to optimization. In this talk, we survey a recent algorithm for sampling known as the Proximal Sampler, which can be seen as a proximal discretization of the continuous-time Langevin dynamics, and achieves the current state-of-the-art iteration complexity for sampling in discrete […]

ICLR 2026 Workshop: Principled Design for Trustworthy AI – Interpretability, Robustness, and Safety across Modalities

ICLR 2026 Riocentro Convention and Event Center, Rio de Janiero, Brazil

Modern AI systems, particularly large language models, vision-language models, and deep vision networks, are increasingly deployed in high-stakes settings such as healthcare, autonomous driving, and legal decisions. Yet, their lack of transparency, fragility to distributional shifts between train/test environments, and representation misalignment in emerging tasks and data/feature modalities raise serious concerns about their trustworthiness. This […]

TILOS-SDSU Seminar: A Modular AgenticAI Architecture for Commercially Scalable and Compliant Robotics

TBA

Sahil Rajesh Dhayalkar, Brain Corporation Abstract: Autonomous navigation in dynamic environments faces immense challenges. Traditional rigid, rules-based systems often fail due to a lack of semantic understanding needed to adapt to continuous environmental shifts. Conversely, emerging end-to-end Vision-Language-Action (VLA) models introduce a critical "black box" dilemma; they inherently lack the explicit application context, deterministic guardrails, […]

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 […]

TILOS-SDSU Seminar: Distributionally Robust Optimization and Applications to Air Traffic Management

Lamden Hall (LH) 247 San Diego State University, San Diego, CA, United States

Max Z. Li, University of Michigan Abstract: Ground Delay Programs (GDPs) mitigate air traffic demand-capacity imbalances by holding flights on the ground at their origin airports when the destination airport's arrival capacity is reduced, thereby limiting costly airborne holding. A central challenge is that day-to-day demand-capacity balancing depends on accurate capacity predictions. In practice, however, […]

Vernor Vinge Lecture Series on the Future of AI and Society with Ted Chiang: No Machine Has Passed the Turing Test

Halicioglu Data Science Institute Room 123 3234 Matthews Ln, La Jolla, CA, United States

It's now commonly asserted that the Turing Test is no longer a useful metric because current LLMs easily pass it. In this talk, Ted Chiang will offer a different interpretation of Turing's paper and what Turing actually meant by a thinking machine. Linguistic fluency of the type that Turing described is a very high bar, […]