8 citations · 11 across the 3 of their papers we have counts for
4 papers
When do data mixtures improve scaling laws? Insights from high-dimensional regression
Diyuan Wu, Lehan Chen, Theodor Misiakiewicz +1
Modern machine learning systems are trained on mixtures of data from different domains, and choosing the right mixture can substantially improve downstream performance. Despite an…
Improved Scaling Laws via Weak-to-Strong Generalization in Random Feature Ridge Regression
Diyuan Wu, Lehan Chen, Theodor Misiakiewicz +1
It is increasingly common in machine learning to use learned models to label data and then employ such data to train more capable models. The phenomenon of weak-to-strong generaliz…
SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics
Emmanuel Abbe, Enric Boix-Adsera, Theodor Misiakiewicz
We investigate the time complexity of SGD learning on fully-connected neural networks with isotropic data. We put forward a complexity measure -- the leap -- which measures how "hi…
The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks
Emmanuel Abbe, Enric Boix-Adsera, Theodor Misiakiewicz
It is currently known how to characterize functions that neural networks can learn with SGD for two extremal parameterizations: neural networks in the linear regime, and neural net…