20 citations · 52 across the 15 of their papers we have counts for
3 papers · 1 filter
Retraining with Predicted Hard Labels Provably Increases Model Accuracy
Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto +5
The performance of a model trained with noisy labels is often improved by simply \textit{retraining} the model with its \textit{own predicted hard labels} (i.e., 1/0 labels). Yet,…
Towards Quantifying the Preconditioning Effect of Adam
Rudrajit Das, Naman Agarwal, Sujay Sanghavi +1
There is a notable dearth of results characterizing the preconditioning effect of Adam and showing how it may alleviate the curse of ill-conditioning -- an issue plaguing gradient…
Understanding the Training Speedup from Sampling with Approximate Losses
Rudrajit Das, Xi Chen, Bertram Ieong +2
It is well known that selecting samples with large losses/gradients can significantly reduce the number of training steps. However, the selection overhead is often too high to yiel…