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DTSTART;TZID=America/New_York:20260506T140000
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SUMMARY:New directions in synthetic data
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Tatsunori Hashimoto\, Stanford \nTitle: New directions in synthetic data \nAbstract: Synthetic data has been an effective\, if boring set of techniques: prompt some language model to restructure your corpus to match some downstream task\, with occasionally some distillation. In this talk\, we will take a more expansive view of synthetic data as a general algorithmic tool for generative modeling\, arguing that the design space and possibilities of synthetic data are much bigger than it might seem. Through a few recent works\, we will show that synthetic data has major benefits beyond transforming the data – improving in-domain perplexities\, and enabling unique algorithmic primitives\, such as neighborhood smoothing and concatenated ‘mega’ documents. With this broader view\, we will point towards a nascent but interesting possibility of treating data itself as an algorithmic object to be engineered and optimized end-to-end. \n 
URL:https://cmsa.fas.harvard.edu/event/newtech_5626/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-5.6.2026.docx.png
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DTSTART;TZID=America/New_York:20260520T140000
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CREATED:20260429T133019Z
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SUMMARY:Separation of timescales controls feature learning and overfitting in large neural networks
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Pierfrancesco Urbani\, Universite Paris-Saclay\, CNRS\, CEA\, Institut de physique theorique \nTitle: Separation of timescales controls feature learning and overfitting in large neural networks \nAbstract: To understand the inductive bias and generalization capabilities of large\, overparameterized machine learning models\, it is essential to analyze the dynamics of their training algorithms. Using dynamical mean field theory we investigate the learning dynamics of large two-layer neural networks. Our findings reveal that\, for networks with a large width\, the training process exhibits a separation of timescales phenomenon. This leads to several key observations:\n1. The emergence of a slow timescale linked to the growth in Gaussian/Rademacher complexity of the network;\n2. An inductive bias favoring low complexity when the initial model complexity is sufficiently small;\n3. A dynamical decoupling between feature learning and overfitting phases;\n4. A non-monotonic trend in test error\, characterized by a “feature unlearning” regime at later stages of training.\nJoint work with Andrea Montanari. \n  \n 
URL:https://cmsa.fas.harvard.edu/event/newtech_52026/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-5.20.2026.docx.png
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