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cs.LG2024
On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
Nirmit Joshi, Theodor Misiakiewicz, Nathan Srebro
The goal of this paper is to investigate the complexity of gradient algorithms when learning sparse functions (juntas). We introduce a type of Statistical Queries (),…
cs.SI2024
Exact Community Recovery under Side Information: Optimality of Spectral Algorithms
Julia Gaudio, Nirmit Joshi
We study the problem of exact community recovery in general, two-community block models, in the presence of node-attributed . We allow for a very general side i…
cs.LG2024★ 1 cited
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari +5
Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD. Despite this success, theoretically…