Matthew S. Nokleby

Optimization & Training · 2020

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 14 citations into it
• Computer Networks and Communications — 11 citations into it
• Artificial Intelligence — 9 citations into it

Whom Matthew S. Nokleby trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yurii E. Nesterov
2. Lin Xiao

Papers from Matthew S. Nokleby's perspective

Ordered by how strongly the researchers Matthew S. Nokleby trusts endorse each paper. Same corpus, a different order for every researcher.

1. Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization
2. Primal-dual subgradient methods for convex problems
3. Parallelized Stochastic Gradient Descent
4. Large Scale Distributed Deep Networks
5. Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
6. Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
7. An asynchronous parallel stochastic coordinate descent algorithm
8. Optimal Distributed Online Prediction Using Mini-Batches
9. Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms
10. Revisiting Distributed Synchronous SGD

Their papers

• Anytime Minibatch: Exploiting Stragglers in Online Distributed Optimization (2020) — 48 citations