274 citations · 3.1k across the 75 of their papers we have counts for
60 papers · 1 filter
On the Limitations of Temperature Scaling for Distributions with Overlaps
Muthu Chidambaram, Rong Ge
Despite the impressive generalization capabilities of deep neural networks, they have been repeatedly shown to be overconfident when they are wrong. Fixing this issue is known as m…
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models
Alex Damian, Eshaan Nichani, Rong Ge +1
We focus on the task of learning a single index model with respect to the isotropic Gaussian distribution in dimensions. Prior work has shown that the samp…
Depth Separation with Multilayer Mean-Field Networks
Yunwei Ren, Mo Zhou, Rong Ge
Depth separation -- why a deeper network is more powerful than a shallower one -- has been a major problem in deep learning theory. Previous results often focus on representation p…
Hiding Data Helps: On the Benefits of Masking for Sparse Coding
Muthu Chidambaram, Chenwei Wu, Yu Cheng +1
Sparse coding, which refers to modeling a signal as sparse linear combinations of the elements of a learned dictionary, has proven to be a successful (and interpretable) approach i…
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression
Mo Zhou, Rong Ge
In deep learning, often the training process finds an interpolator (a solution with 0 training loss), but the test loss is still low. This phenomenon, known as benign overfitting,…
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup
Muthu Chidambaram, Xiang Wang, Chenwei Wu +1
Mixup is a data augmentation technique that relies on training using random convex combinations of data points and their labels. In recent years, Mixup has become a standard primit…