9 citations · 21 across the 14 of their papers we have counts for
5 papers · 1 filter
Generalization, memorization, and overfitting for diffusion models trained in the lazy high-dimensional regime
Hugo Latourelle-Vigeant, Sinho Chewi, Aram-Alexandre Pooladian +2
Modern score-based generative models have achieved remarkable empirical success in high-dimensional tasks such as image, audio, and video synthesis. These models reduce distributio…
A Theory of Learning with Autoregressive Chain of Thought
Nirmit Joshi, Gal Vardi, Adam Block +4
For a given base class of sequence-to-next-token generators, we consider learning prompt-to-answer mappings obtained by iterating a fixed, time-invariant generator for multiple ste…
Dimension-free deterministic equivalents and scaling laws for random feature regression
Leonardo Defilippis, Bruno Loureiro, Theodor Misiakiewicz
In this work we investigate the generalization performance of random feature ridge regression (RFRR). Our main contribution is a general deterministic equivalent for the test error…
A non-asymptotic theory of Kernel Ridge Regression: deterministic equivalents, test error, and GCV estimator
Theodor Misiakiewicz, Basil Saeed
We consider learning an unknown target function using kernel ridge regression (KRR) given i.i.d. data , , where is a covariate vector and $y_i…
Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime
Hong Hu, Yue M. Lu, Theodor Misiakiewicz
Recent advances in machine learning have been achieved by using overparametrized models trained until near interpolation of the training data. It was shown, e.g., through the doubl…