10 citations · 10 across the 5 of their papers we have counts for
5 papers
Geometric Median Matching for Robust k-Subset Selection from Noisy Data
Anish Acharya, Sujay Sanghavi, Alexandros G. Dimakis +1
Data pruning -- the combinatorial task of selecting a small and representative subset from a large dataset, is crucial for mitigating the enormous computational costs associated wi…
Geometric Median (GM) Matching for Robust Data Pruning
Anish Acharya, Inderjit S Dhillon, Sujay Sanghavi
Large-scale data collections in the wild, are invariably noisy. Thus developing data pruning strategies that remain robust even in the presence of corruption is critical in practic…
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…
Faster Non-Convex Federated Learning via Global and Local Momentum
Rudrajit Das, Anish Acharya, Abolfazl Hashemi +3
We propose \texttt{FedGLOMO}, a novel federated learning (FL) algorithm with an iteration complexity of to converge to an -stationary point (i.e., $\math…