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stat.ML2026
A theory of learning data statistics in diffusion models, from easy to hard
Lorenzo Bardone, Claudia Merger, Sebastian Goldt
While diffusion models have emerged as a powerful class of generative models, their learning dynamics remain poorly understood. We address this issue first by empirically showing t…
stat.ML2026
A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights
Fabiola Ricci, Claudia Merger, Sebastian Goldt
Neural networks trained with gradient-based methods exhibit a strong simplicity bias: they learn simpler statistical features of their data before moving to more complex features.…
stat.ML2026
Generalization Dynamics of Linear Diffusion Models
Claudia Merger, Sebastian Goldt
Diffusion models are powerful generative models that produce high-quality samples from complex data. While their infinite-data behavior is well understood, their generalization wit…