research
∙
09/04/2023
Les Houches Lectures on Deep Learning at Large Infinite Width
These lectures, presented at the 2022 Les Houches Summer School on Stati...
share
research
∙
06/22/2023
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression
We study the cost of overfitting in noisy kernel ridge regression (KRR),...
research
∙
06/13/2023
Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training
Hyperparameter tuning of deep learning models can lead to order-of-magni...
research
∙
03/27/2023
On the stepwise nature of self-supervised learning
We present a simple picture of the training process of self-supervised l...
research
∙
10/24/2022
Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds
Despite impressive successes, deep reinforcement learning (RL) systems s...
research
∙
09/04/2022
On Kernel Regression with Data-Dependent Kernels
The primary hyperparameter in kernel regression (KR) is the choice of ke...
research
∙
07/14/2022
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting
The practical success of overparameterized neural networks has motivated...
research
∙
10/08/2021
Neural Tangent Kernel Eigenvalues Accurately Predict Generalization
Finding a quantitative theory of neural network generalization has long ...
research
∙
06/06/2021
