Sashank Reddi | DeepAI

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∙ 06/22/2023

The Inductive Bias of Flatness Regularization for Deep Matrix Factorization

Recent works on over-parameterized neural networks have shown that the s...

0 Khashayar Gatmiry, et al. ∙

∙ 01/28/2022

FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients

In classical federated learning, the clients contribute to the overall t...

0 Jianyu Wang, et al. ∙

∙ 05/12/2021

Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces

Negative sampling schemes enable efficient training given a large number...

2 Ankit Singh Rawat, et al. ∙

∙ 11/17/2020

Federated Composite Optimization

Federated Learning (FL) is a distributed learning paradigm which scales ...

0 Honglin Yuan, et al. ∙

∙ 02/29/2020

Adaptive Federated Optimization

Federated learning is a distributed machine learning paradigm in which a...

28 Sashank Reddi, et al. ∙

∙ 02/20/2020

Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets

We study distributed optimization algorithms for minimizing the average ...

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∙ 10/21/2019

Learning to Learn by Zeroth-Order Oracle

In the learning to learn (L2L) framework, we cast the design of optimiza...

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