Lam M. Nguyen

Optimization & Training · 2017–2023

A researcher in the Science corpus: 9 papers; trusts 92 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 108 citations into it; #743 of 2,601 by trust here, #999 by citations
• Artificial Intelligence — 107 citations into it; #6,086 of 35,345 by trust here, #11,072 by citations
• Computational Mechanics — 62 citations into it; #1,239 of 10,001 by trust here, #2,032 by citations
• Management Science and Operations Research — 25 citations into it
• Statistics and Probability — 25 citations into it; #2,179 of 12,932 by trust here, #2,693 by citations
• Computational Theory and Mathematics — 21 citations into it; #10,281 of 12,182 by trust here, #4,850 by citations
• Computer Vision and Pattern Recognition — 19 citations into it
• Statistical Learning Theory — 12 citations into it

Whom Lam M. Nguyen trusts overall

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

1. L. Eon Bottou
2. Yurii E. Nesterov
3. Dimitri P. Bertsekas
4. Alex Krizhevsky
5. Francis R. Bach
6. Boris Polyak
7. Chih-Jen Lin
8. Tong Zhang
9. Rie Johnson
10. Aaron Defazio

Who trusts Lam M. Nguyen overall

1. Peter Richtárik
2. Samuel Horváth
3. Sai Praneeth Karimireddy
4. Nicolas Loizou
5. Quoc Tran-Dinh
6. Robert Mansel Gower
7. Ahmed Khaled
8. Konstantin Mishchenko
9. Albert S. Berahas
10. Cong Fang

Papers from Lam M. Nguyen's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient (2017) — 717 citations
• SGD and Hogwild! Convergence Without the Bounded Gradients Assumption (2018) — 251 citations
• Stochastic Recursive Gradient Algorithm for Nonconvex Optimization (2017) — 100 citations
• Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization (2019) — 60 citations
• Label-Free Concept Bottleneck Models (2023) — 334 citations
• Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function (2020) — 28 citations
• A Unified Convergence Analysis for Shuffling-Type Gradient Methods (2020) — 100 citations
• A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees (2020) — 46 citations
• New Convergence Aspects of Stochastic Gradient Algorithms (2018) — 78 citations