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DTSTART;TZID=America/Los_Angeles:20260515T110000
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DTSTAMP:20260525T092309
CREATED:20260413T175443Z
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SUMMARY:Optimization for ML and AI Seminar: A non-equilibrium phase transition with broken ergodicity leads to double descent and benign overfitting in machine learning
DESCRIPTION:Nigel Goldenfeld\, UC San Diego Department of Physics and HDSI \nAbstract: 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 diverges when the number of model parameters approaches a critical value from below. Here we use dynamical mean field theory to show\, in a simple setting of linear regression\, that this so-called “double descent” behavior is the outcome of a phase transition in the stochastic field theory describing the training process. We calculate the critical exponents and scaling function of the double descent phase transition\, and show that it is marked by a breakdown of the fluctuation-dissipation theorem associated with broken ergodicity. The corresponding response function has the same functional form as the simple London model of the superconducting transition\, with the rigidity of the wave function corresponding to the neural network’s ability to generalize accurately. Our results are distinct from earlier work\, because we calculate the time-dependence specifically\, not just the equilibrium solutions. This is what enables us to identify the origin of the emergent behavior. \nWork performed with Chan Li. \n\nNigel Goldenfeld holds the Chancellor’s Distinguished Professorship in Physics at UC San Diego\, which he joined in Fall 2021 after 36 years at the University of Illinois at Urbana-Champaign (UIUC). Nigel’s research spans condensed matter theory\, the theory of living systems\, hydrodynamics and non-equilibrium statistical physics.  \nNigel received his PhD in theoretical physics from the University of Cambridge (UK) in 1982\, and from 1982-1985 was a postdoctoral fellow at the Institute for Theoretical Physics at UC Santa Barbara\, where his work on the dynamics of snowflake growth helped launch the modern theory of pattern formation in nature. He joined the condensed matter theory group at the Department of Physics at UIUC in 1985\, where his work was instrumental to the discovery of d-wave pairing in high temperature superconductors. Nigel’s interests in biology include microbial ecology\, evolution and systems biology. He was a founding member of the Institute for Genomic Biology at UIUC\, where he led the Biocomplexity Group and directed the NASA Astrobiology Institute for Universal Biology. During the COVID-19 pandemic\, he pivoted from his experience in mathematical modeling of bacteria and viruses to computational epidemiology\, advising the Governor of Illinois\, and helping devise\, set up and run the COVID saliva testing system at UIUC\, which provided ~12 hour turnaround of PCR tests to the 50\,000 people in the campus community and eventually to over 1700 schools and other institutions in Illinois and beyond. Nigel has served on the editorial boards of several journals\, including The Philosophical Transactions of the Royal Society\, Physical Biology and the International Journal of Theoretical and Applied Finance. Selected honors include: Alfred P. Sloan Foundation Fellow\, University Scholar of the University of Illinois\, the Xerox Award for research\, the A. Nordsieck award for excellence in graduate teaching and the American Physical Society’s Leo P. Kadanoff Prize 2020. Nigel is a Fellow of the American Physical Society\, a Fellow of the American Academy of Arts and Sciences\, a Fellow of the Royal Society (UK) and a Member of the US National Academy of Sciences.
URL:https://tilos.ai/event/optimization-for-ml-and-ai-seminar-a-non-equilibrium-phase-transition-with-broken-ergodicity-leads-to-double-descent-and-benign-overfitting-in-machine-learning/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2026/04/goldenfeld-nigel-e1776102861254.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260520T110000
DTEND;TZID=America/Los_Angeles:20260520T120000
DTSTAMP:20260525T092309
CREATED:20260227T004426Z
LAST-MODIFIED:20260520T204734Z
UID:8112-1779274800-1779278400@tilos.ai
SUMMARY:TILOS-HDSI Seminar: Inference-Time Algorithms: A Theoretical Lens on Tractability and Error Propagation
DESCRIPTION:Andrej Risteski\, Carnegie Mellon University \nAbstract: Modern AI systems are increasingly built by placing trained models inside larger computational loops. Inference-time algorithms are a basic instance of this idea: they use one or more trained models at test time to incorporate new information\, exploit pretrained models as priors\, and trade computational effort for accuracy\, sample quality\, or control. Examples include generator-verifier search for reasoning\, diffusion models for solving inverse problems\, and reward-guided generation. Theoretically\, this revisits a classical question from optimization and theoretical computer science: what can be done with access to an oracle? Here\, however\, the oracles are new and non-standard: they model the capabilities of large pretrained models\, making them powerful\, but also imperfect because they are learned. This combination leads to new questions about algorithm design and error propagation. \nThis talk studies two central aspects of this paradigm: computational efficiency and error propagation. The first vignette considers generator-verifier systems\, and shows how stochastic backtracking can trade additional computation for accuracy\, giving a principled version of test-time scaling even with imperfect learned oracles. The second vignette studies diffusion steering: when can we efficiently bias a pretrained diffusion model toward higher-reward samples while staying close to the original model? We show that tractability depends strongly on both the reward structure and the alignment objective\, and that simple primitives—such as sampling from linear tilts—can be surprisingly useful for handling richer reward classes. \nBased on https://arxiv.org/abs/2510.03149\, https://arxiv.org/abs/2602.16570\, https://arxiv.org/abs/2605.11361. \n\nAndrej Risteski is an Associate Professor at the Machine Learning Department in Carnegie Mellon University. Prior to that\, he was a Norbert Wiener Research Fellow jointly in the Applied Math department and IDSS at MIT. Dr. Risteski received his PhD in the Computer Science Department at Princeton University under the advisement of Sanjeev Arora. \nDr. Risteski’s research interests lie in the intersection of machine learning\, statistics\, and theoretical computer science\, spanning topics like (probabilistic) generative models\, algorithmic tools for learning and inference\, representation and self-supervised learning\, out-of-distribution generalization and applications of neural approaches to natural language processing and scientific domains. The broad goal of his research is principled and mathematical understanding of statistical and algorithmic problems arising in modern machine learning paradigms.
URL:https://tilos.ai/event/tilos-hdsi-seminar-inference-time-algorithms-a-theoretical-lens-on-tractability-and-error-propagation/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/png:https://tilos.ai/wp-content/uploads/2026/02/risteski-andrej-e1772152946152.png
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20260603
DTEND;VALUE=DATE:20260604
DTSTAMP:20260525T092309
CREATED:20260224T210719Z
LAST-MODIFIED:20260422T171711Z
UID:8102-1780444800-1780531199@tilos.ai
SUMMARY:CVPR 2026 Workshop: Trustworthy\, Robust\, Uncertainty-Aware\, and Explainable Visual Intelligence and Beyond (TRUE-V)
DESCRIPTION: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 research on interpretability\, robustness\, uncertainty\, and alignment under a unified design paradigm\, encouraging cross-disciplinary exchange on shared technical and societal challenges. By promoting responsible design and deployment\, the workshop seeks to advance forward-looking solutions for visual intelligence that enhance accountability and public trust.
URL:https://tilos.ai/event/cvpr-2026-workshop/
LOCATION:IEEE/CVF Conference on Computer Vision and Pattern Recognition\, Denver\, CO\, United States
CATEGORIES:TILOS Sponsored Event,Workshop
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2026/02/CVPR_Denver_2026.jpg
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