Scott Sallinen

Optimization & Training · 2015

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Optimization & Training — 10 citations into it; #2,486 of 2,601 by trust here, #1,537 by citations
• Artificial Intelligence — 8 citations into it; #32,863 of 35,345 by trust here, #17,110 by citations
• Computational Mechanics — 8 citations into it

Whom Scott Sallinen trusts overall

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

1. L. Eon Bottou

Who trusts Scott Sallinen overall

1. Jakub Konecný

Papers from Scott Sallinen's perspective

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

1. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
2. The Tradeoffs of Large Scale Learning
3. Large-Scale Machine Learning with Stochastic Gradient Descent
4. SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
5. Hybrid Deterministic-Stochastic Methods for Data Fitting
6. Stochastic dual coordinate ascent methods for regularized loss
7. A Proximal Stochastic Gradient Method with Progressive Variance Reduction
8. Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
9. Linear Convergence with Condition Number Independent Access of Full Gradients
10. Sample size selection in optimization methods for machine learning

Their papers

• StopWasting My Gradients: Practical SVRG (2015) — 139 citations