Lam M. Nguyen
Lam M. Nguyen
Research Scientist, IBM Thomas J. Watson Research Center
確認したメール アドレス: ibm.com - ホームページ
タイトル引用先
SARAH: A novel method for machine learning problems using stochastic recursive gradient
LM Nguyen, J Liu, K Scheinberg, M Takáč
The 34th International Conference on Machine Learning (ICML 2017), 2017
612017
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
LM Nguyen, PH Nguyen, M van Dijk, P Richtárik, K Scheinberg, M Takác
The 35th International Conference on Machine Learning (ICML 2018), 2018
212018
Stochastic recursive gradient algorithm for nonconvex optimization
LM Nguyen, J Liu, K Scheinberg, M Takáč
arXiv preprint arXiv:1705.07261, 2017
212017
CEO Compensation: Does Financial Crisis Matter?
P Vemala, L Nguyen, D Nguyen, A Kommasani
International Business Research 7 (4), 125-131, 2014
172014
When does stochastic gradient algorithm work well?
LM Nguyen, NH Nguyen, DT Phan, JR Kalagnanam, K Scheinberg
arXiv preprint arXiv:1801.06159, 2018
72018
A service system with randomly behaving on-demand agents
LM Nguyen, AL Stolyar
SIGMETRICS 2016, ACM SIGMETRICS Performance Evaluation Review 44 (1), 365-366, 2016
72016
Inexact SARAH algorithm for stochastic optimization
LM Nguyen, K Scheinberg, M Takáč
arXiv preprint arXiv:1811.10105, 2018
32018
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
NH Pham, LM Nguyen, DT Phan, Q Tran-Dinh
arXiv preprint arXiv:1902.05679, 2019
22019
Optimal finite-sum smooth non-convex optimization with SARAH
LM Nguyen, M van Dijk, DT Phan, PH Nguyen, TW Weng, ...
arXiv preprint arXiv:1901.07648, 2019
22019
New convergence aspects of stochastic gradient algorithms
LM Nguyen, PH Nguyen, P Richtárik, K Scheinberg, M Takáč, M van Dijk
arXiv preprint arXiv:1811.12403, 2018
22018
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
M van Dijk, LM Nguyen, PH Nguyen, DT Phan
The 36th International Conference on Machine Learning (ICML 2019), 2019
12019
A queueing system with on-demand servers: local stability of fluid limits
L Nguyen, A Stolyar
Queueing Systems: Theory and Applications 89 (3-4), 243–268, 2017
12017
Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization
Q Tran-Dinh, NH Pham, DT Phan, LM Nguyen
arXiv preprint arXiv:1905.05920, 2019
2019
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
TW Weng, PY Chen, LM Nguyen, MS Squillante, A Boopathy, I Oseledets, ...
The 36th International Conference on Machine Learning (ICML 2019), 2019
2019
Finite-Sum Smooth Optimization with SARAH
LM Nguyen, M van Dijk, DT Phan, PH Nguyen, TW Weng, ...
arXiv preprint, 2019
2019
DTN: A Learning Rate Scheme with Convergence Rate of for SGD
LM Nguyen, PH Nguyen, DT Phan, JR Kalagnanam, M van Dijk
arXiv preprint arXiv:1901.07634, 2019
2019
ChieF: A Change Pattern based Interpretable Failure Analyzer
D Patel, LM Nguyen, A Rangamani, S Shrivastava, J Kalagnanam
2018 IEEE International Conference on Big Data (Big Data), 1978-1985, 2018
2018
Tight Dimension Independent Lower Bound on Optimal Expected Convergence Rate for Diminishing Step Sizes in SGD
PH Nguyen, LM Nguyen, M van Dijk
arXiv preprint arXiv:1810.04723, 2018
2018
A Service System with On-Demand Agents, Stochastic Gradient Algorithms and the SARAH Algorithm
L Nguyen
Lehigh University, 2018
2018
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