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Found 5 papers, 1 papers with code
Gradient Coding: Avoiding Stragglers in Distributed Learning
We propose a novel coding theoretic framework for mitigating stragglers in distributed learning.
Gradient Coding from Cyclic MDS Codes and Expander Graphs
Gradient coding is a technique for straggler mitigation in distributed learning.
Gradient Coding
We propose a novel coding theoretic framework for mitigating stragglers in distributed learning.
Kernel Ridge Regression via Partitioning
In this paper, we investigate a divide and conquer approach to Kernel Ridge Regression (KRR).
On the Information Theoretic Limits of Learning Ising Models
We provide a general framework for computing lower-bounds on the sample complexity of recovering the underlying graphs of Ising models, given i. i. d samples.
