Lam M. Nguyen

Generating author description...

All published works

Action	Title	Year	Authors
+ PDF Chat	Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification	2024	Yunshi Wen Tengfei Ma Tsui-Wei Weng Lam M. Nguyen A. Agung Julius
+ PDF Chat	Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization	2024	Quoc Tran-Dinh Trang H. Tran Lam M. Nguyen
+ PDF Chat	Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks	2024	Albert V. Baez Wang Zhang Ziwen Ma Subhro Das Lam M. Nguyen Luca Daniel
+	Shuffling Momentum Gradient Algorithm for Convex Optimization	2024	Trang H. Tran Quoc Tran-Dinh Lam M. Nguyen
+ PDF Chat	One Step Closer to Unbiased Aleatoric Uncertainty Estimation	2024	Zhang Wang Ziwen Martin Subhro Das Tsui-Wei Weng Alexandre Megretski Luca Daniel Lam M. Nguyen
+ PDF Chat	Shuffling Momentum Gradient Algorithm for Convex Optimization	2024	Trang H. Tran Quoc Tran-Dinh Lam M. Nguyen
+ PDF Chat	Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization	2024	Lam M. Nguyen Katya Scheinberg Trang H. Tran
+ PDF Chat	Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches	2023	Linbo Liu Trong Nghia Hoang Lam M. Nguyen Tsui-Wei Weng
+ PDF Chat	Attacking c-MARL More Effectively: A Data Driven Approach	2023	Nhan H. Pham Lam M. Nguyen Jie Chen Hoang Thanh Lam Subhro Das Tsui-Wei Weng
+	ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction	2023	Wang Zhang Tsui-Wei Weng Subhro Das Alexandre Megretski Luca Daniel Lam M. Nguyen
+	An End-to-End Time Series Model for Simultaneous Imputation and Forecast	2023	Trang H. Tran Lam M. Nguyen Kyongmin Yeo Nam Nguyen Dzung T. Phan Roman Vaculín Jayant Kalagnanam
+	Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach	2023	Anh Nguyen Trang H. Tran Hieu H. Pham Phi Le Nguyen Lam M. Nguyen
+	Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent	2023	Toan N. Nguyen Phuong Ha Nguyen Lam M. Nguyen Marten van Dijk
+	Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches	2023	Linbo Liu Trong Nghia Hoang Lam M. Nguyen Tsui-Wei Weng
+	A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series	2023	Trang H. Tran Lam M. Nguyen Kyongmin Yeo Nam Khanh Nguyen Roman Vaculín
+	One step closer to unbiased aleatoric uncertainty estimation	2023	Wang Zhang Ziwen Ma Subhro Das Tsui-Wei Weng Alexandre Megretski Luca Daniel Lam M. Nguyen
+ PDF Chat	Interpretable Clustering via Multi-Polytope Machines	2022	Connor Lawless Jayant Kalagnanam Lam M. Nguyen Dzung T. Phan Chandra Reddy
+ PDF Chat	Finite-sum smooth optimization with SARAH	2022	Lam M. Nguyen Marten van Dijk Dzung T. Phan Phuong Ha Nguyen Tsui-Wei Weng Jayant Kalagnanam
+	Attacking c-MARL More Effectively: A Data Driven Approach	2022	Nhan H. Pham Lam M. Nguyen Jie Chen Hoang Thanh Lam Subhro Das Tsui-Wei Weng
+	Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution	2022	Lam M. Nguyen Trang H. Tran Marten van Dijk
+	Nesterov Accelerated Shuffling Gradient Method for Convex Optimization	2022	Trang H. Tran Lam M. Nguyen Katya Scheinberg
+	StepDIRECT -- A Derivative-Free Optimization Method for Stepwise Functions	2022	Dzung T. Phan Huan Liu Lam M. Nguyen
+ PDF Chat	StepDIRECT - A Derivative-Free Optimization Method for Stepwise Functions	2022	Dzung T. Phan Huan Liu Lam M. Nguyen
+	Finding Optimal Policy for Queueing Models: New Parameterization	2022	Trang H. Tran Lam M. Nguyen Katya Scheinberg
+	Generalizing DP-SGD with Shuffling and Batch Clipping	2022	Marten van Dijk Phuong Ha Nguyen Toan N. Nguyen Lam M. Nguyen
+	On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error	2022	Quang Minh Nhi Nguyen Hoang Huy Nguyen Yi Zhou Lam M. Nguyen
+	On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms	2022	Lam M. Nguyen Trang H. Tran
+ PDF Chat	Interpretable Clustering via Multi-Polytope Machines	2021	Connor Lawless Jayant Kalagnanam Lam M. Nguyen Dzung T. Phan Chandra Reddy
+	On the Equivalence between Neural Network and Support Vector Machine	2021	Yilan Chen Wei Huang Lam M. Nguyen Tsui-Wei Weng
+ PDF Chat	On the Equivalence between Neural Network and Support Vector Machine	2021	Yilan Chen Wei Huang Lam M. Nguyen Tsui-Wei Weng
+	Federated Learning with Randomized Douglas-Rachford Splitting Methods.	2021	Nhan H. Pham Lam M. Nguyen Dzung T. Phan Quoc Tran-Dinh
+	FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization.	2021	Quoc Tran-Dinh Nhan H. Pham Dzung T. Phan Lam M. Nguyen
+	Differential Private Hogwild! over Distributed Local Data Sets.	2021	Marten van Dijk Nhuong V. Nguyen Toan N. Nguyen Lam M. Nguyen Phuong Ha Nguyen
+	Bringing Differential Private SGD to Practice: On the Independence of Gaussian Noise and the Number of Training Rounds	2021	Marten van Dijk Nhuong V. Nguyen Toan N. Nguyen Lam M. Nguyen Phuong Ha Nguyen
+	On the Equivalence between Neural Network and Support Vector Machine	2021	Yilan Chen Wei Huang Lam M. Nguyen Tsui-Wei Weng
+	FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization	2021	Quoc Tran-Dinh Nhan H. Pham Dzung T. Phan Lam M. Nguyen
+	Proactive DP: A Multple Target Optimization Framework for DP-SGD	2021	Marten van Dijk Nhuong V. Nguyen Toan N. Nguyen Lam M. Nguyen Phuong Ha Nguyen
+	Interpretable Clustering via Multi-Polytope Machines	2021	Connor Lawless Jayant Kalagnanam Lam M. Nguyen Dzung T. Phan Chandra Reddy
+	Shuffling Gradient-Based Methods with Momentum.	2020	Trang H. Tran Lam M. Nguyen Quoc Tran-Dinh
+	SMG: A Shuffling Gradient-Based Method with Momentum	2020	Trang H. Tran Lam M. Nguyen Quoc Tran-Dinh
+ PDF Chat	Inexact SARAH algorithm for stochastic optimization	2020	Lam M. Nguyen Katya Scheinberg Martin Takáč
+	Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization	2020	Quoc Tran-Dinh Nhan H. Pham Lam M. Nguyen
+	Convergence Rates of Accelerated Markov Gradient Descent with Applications in Reinforcement Learning	2020	Thinh T. Doan Lam M. Nguyen Nhan H. Pham Justin Romberg
+	A Unified Convergence Analysis for Shuffling-Type Gradient Methods	2020	Lam M. Nguyen Quoc Tran-Dinh Dzung T. Phan Phuong Ha Nguyen Marten van Dijk
+	A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning	2020	Nhan H. Pham Lam M. Nguyen Dzung T. Phan Phuong Ha Nguyen Marten van Dijk Quoc Tran-Dinh
+	Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness	2020	Thinh T. Doan Lam M. Nguyen Nhan H. Pham Justin Romberg
+	Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems	2020	Quoc Tran-Dinh Deyi Liu Lam M. Nguyen
+	Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise	2020	Marten van Dijk Nhuong V. Nguyen Toan N. Nguyen Lam M. Nguyen Quoc Tran-Dinh Phuong Ha Nguyen
+	An Optimal Hybrid Variance-Reduced Algorithm for Stochastic Composite Nonconvex Optimization	2020	Deyi Liu Lam M. Nguyen Quoc Tran-Dinh
+	Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes	2020	Marten van Dijk Nhuong V. Nguyen Toan N. Nguyen Lam M. Nguyen Quoc Tran-Dinh Phuong Ha Nguyen
+	A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees	2020	Haoran Zhu Pavankumar Murali Dzung T. Phan Lam M. Nguyen Jayant Kalagnanam
+	SMG: A Shuffling Gradient-Based Method with Momentum	2020	Trang Tran Lam M. Nguyen Quoc Tran-Dinh
+	Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization	2020	Quoc Tran-Dinh Nhan H. Pham Lam M. Nguyen
+	Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD	2019	Marten van Dijk Lam M. Nguyen Phuong Ha Nguyen Dzung T. Phan
+	Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization	2019	Quoc Tran-Dinh Nhan H. Pham Dzung T. Phan Lam M. Nguyen
+	Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH.	2019	Lam M. Nguyen Marten van Dijk Dzung T. Phan Phuong Ha Nguyen Tsui-Wei Weng Jayant Kalagnanam
+	DTN: A Learning Rate Scheme with Convergence Rate of $\mathcal{O}(1/t)$ for SGD	2019	Lam M. Nguyen Phuong Ha Nguyen Dzung T. Phan Jayant Kalagnanam Marten van Dijk
+	ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization	2019	Nhan H. Pham Lam M. Nguyen Dzung T. Phan Quoc Tran-Dinh
+	A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization	2019	Quoc Tran-Dinh Nhan H. Pham Dzung T. Phan Lam M. Nguyen
+	Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD	2019	Phuong Ha Nguyen Lam M. Nguyen Marten van Dijk
+	BUZz: BUffer Zones for defending adversarial examples in image classification	2019	Phuong Ha Nguyen Kaleel Mahmood Lam M. Nguyen Thanh Thi Nguyen Marten van Dijk
+	New Convergence Aspects of Stochastic Gradient Algorithms	2019	Lam M. Nguyen Phuong Ha Nguyen Peter Richtárik Katya Scheinberg Martin Takáč Marten van Dijk
+	Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization	2019	Quoc Tran-Dinh Nhan H. Pham Dzung T. Phan Lam M. Nguyen
+	DTN: A Learning Rate Scheme with Convergence Rate of $\mathcal{O}(1/t)$ for SGD	2019	Lam M. Nguyen Phuong Ha Nguyen Dzung T. Phan Jayant Kalagnanam Marten van Dijk
+	Finite-Sum Smooth Optimization with SARAH	2019	Lam M. Nguyen Marten van Dijk Dzung T. Phan Phuong Ha Nguyen Tsui-Wei Weng Jayant Kalagnanam
+	PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach	2018	Tsui-Wei Weng Pin‐Yu Chen Lam M. Nguyen Mark S. Squillante Ivan Oseledets Luca Daniel
+	Inexact SARAH Algorithm for Stochastic Optimization	2018	Lam M. Nguyen Katya Scheinberg Martin Takáč
+	New Convergence Aspects of Stochastic Gradient Algorithms	2018	Lam M. Nguyen Phuong Ha Nguyen Peter Richtárik Katya Scheinberg Martin Takáč Marten van Dijk
+	Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD	2018	Marten van Dijk Lam M. Nguyen Phuong Ha Nguyen Dzung T. Phan
+	When Does Stochastic Gradient Algorithm Work Well?	2018	Lam M. Nguyen Nam Nguyen Dzung T. Phan Jayant Kalagnanam Katya Scheinberg
+	SGD and Hogwild! Convergence Without the Bounded Gradients Assumption	2018	Lam M. Nguyen Phuong Ha Nguyen Marten van Dijk Peter Richtárik Katya Scheinberg Martin Takáč
+	Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD	2018	Phuong Ha Nguyen Lam M. Nguyen Marten van Dijk
+	PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach	2018	Tsui-Wei Weng Pin‐Yu Chen Lam M. Nguyen Mark S. Squillante Ivan Oseledets Luca Daniel
+	Inexact SARAH Algorithm for Stochastic Optimization	2018	Lam M. Nguyen Katya Scheinberg Martin Takáč
+	New Convergence Aspects of Stochastic Gradient Algorithms	2018	Lam M. Nguyen Phuong Ha Nguyen Peter Richtárik Katya Scheinberg Martin Takáč Marten van Dijk
+	Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD	2018	Marten van Dijk Lam M. Nguyen Phuong Ha Nguyen Dzung T. Phan
+ PDF Chat	A queueing system with on-demand servers: local stability of fluid limits	2017	Lam M. Nguyen Alexander Stolyar
+	SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient	2017	Lam M. Nguyen Jie Liu Katya Scheinberg Martin Takáč
+	Stochastic Recursive Gradient Algorithm for Nonconvex Optimization	2017	Lam M. Nguyen Jie Liu Katya Scheinberg Martin Takáč
+	SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient	2017	Lam M. Nguyen Jie Liu Katya Scheinberg Martin Takáč
+ PDF Chat	A Service System with Randomly Behaving On-demand Agents	2016	Lam M. Nguyen Alexander Stolyar
+ PDF Chat	A Service System with Randomly Behaving On-demand Agents	2016	Lam M. Nguyen Alexander Stolyar

Common Coauthors

Coauthor	Papers Together
Marten van Dijk	25
Dzung T. Phan	25
Phuong Ha Nguyen	22
Tsui-Wei Weng	16
Katya Scheinberg	14
Nhan H. Pham	14
Jayant Kalagnanam	11
Quoc Tran-Dinh	11
Martin Takáč	10
Quoc Tran-Dinh	10
Toan N. Nguyen	7
Subhro Das	6
Luca Daniel	6
Trang H. Tran	5
Nhuong V. Nguyen	5
Peter Richtárik	4
Trang H. Tran	4
Connor Lawless	3
Yilan Chen	3
Alexander Stolyar	3
Trang H. Tran	3
Alexandre Megretski	3
Thinh T. Doan	2
Ivan Oseledets	2
Mark S. Squillante	2
Hoang Thanh Lam	2
Linbo Liu	2
Roman Vaculín	2
Trong Nghia Hoang	2
Kyongmin Yeo	2
Justin Romberg	2
Deyi Liu	2
Pin‐Yu Chen	2
Wang Zhang	2
Huan Liu	2
Wei Huang	2
Jie Liu	2
Phuong Ha Nguyen	2
Chandra Reddy	2
Thanh Thi Nguyen	1
Tengfei Ma	1
Ziwen Martin	1
Wang Zhang	1
Nam Khanh Nguyen	1
Jie Chen	1
Ziwen Ma	1
Wei Huang	1
Jie Liu	1
Ziwen Ma	1
Zhang Wang	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient	2017	Lam M. Nguyen Jie Liu Katya Scheinberg Martin Takáč	17
+	Optimization Methods for Large-Scale Machine Learning	2018	Léon Bottou Frank E. Curtis Jorge Nocedal	12
+	SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives	2014	Aaron Defazio Francis Bach Simon Lacoste-Julien	11
+	A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets	2012	Nicolas Le Roux Mark Schmidt Francis Bach	11
+	SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator	2018	Cong Fang Chris Junchi Li Zhouchen Lin Tong Zhang	10
+ PDF Chat	Minimizing finite sums with the stochastic average gradient	2016	Mark Schmidt Nicolas Le Roux Francis Bach	9
+ PDF Chat	Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming	2013	Saeed Ghadimi Guanghui Lan	8
+	Introductory Lectures on Convex Optimization: A Basic Course	2014	Ю Е Нестеров	8
+	Stochastic Recursive Gradient Algorithm for Nonconvex Optimization	2017	Lam M. Nguyen Jie Liu Katya Scheinberg Martin Takáč	8
+	Robust Stochastic Approximation Approach to Stochastic Programming	2009	Arkadi Nemirovski Anatoli Juditsky Guanghui Lan Alexander Shapiro	7
+	Non-convex Finite-Sum Optimization Via SCSG Methods	2017	Lihua Lei Cheng Ju Jianbo Chen Michael I. Jordan	7
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	7
+	Some methods of speeding up the convergence of iteration methods	1964	B. T. Polyak	7
+	Stochastic Variance Reduction for Nonconvex Optimization	2016	Sashank J. Reddi Ahmed Hefny Suvrit Sra Barnabás Póczos Alex Smola	6
+	Semi-Stochastic Gradient Descent Methods	2013	Jakub Konečný Peter Richtárik	6
+	Cubic regularization of Newton method and its global performance	2006	Yurii Nesterov B. T. Polyak	6
+ PDF Chat	Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition	2016	Hamed Karimi Julie Nutini Mark Schmidt	6
+	Optimization Methods for Large-Scale Machine Learning	2016	Léon Bottou Frank E. Curtis Jorge Nocedal	6
+ PDF Chat	Finite-sum smooth optimization with SARAH	2022	Lam M. Nguyen Marten van Dijk Dzung T. Phan Phuong Ha Nguyen Tsui-Wei Weng Jayant Kalagnanam	5
+	When Does Stochastic Gradient Algorithm Work Well?	2018	Lam M. Nguyen Nam Nguyen Dzung T. Phan Jayant Kalagnanam Katya Scheinberg	5
+ PDF Chat	Katyusha: the first direct acceleration of stochastic gradient methods	2017	Zeyuan Allen-Zhu	4
+	Momentum-Based Variance Reduction in Non-Convex SGD	2019	Ashok Cutkosky Francesco Orabona	4
+	Stochastic Variance Reduction for Nonconvex Optimization	2016	Sashank J. Reddi Ahmed Hefny Suvrit Sra Barnabás Póczos Alex Smola	4
+ PDF Chat	Stochastic Frank-Wolfe methods for nonconvex optimization	2016	Sashank J. Reddi Suvrit Sra Barnabás Póczos Alex Smola	4
+	A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization	2019	Quoc Tran-Dinh Nhan H. Pham Dzung T. Phan Lam M. Nguyen	4
+ PDF Chat	Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey	2011	Dimitri P. Bertsekas	4
+	HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent	2011	Feng Niu Benjamin Recht Christopher Ré Stephen J. Wright	4
+	SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization.	2018	Zhe Wang Kaiyi Ji Yi Zhou Yingbin Liang Vahid Tarokh	4
+	Local SGD Converges Fast and Communicates Little	2018	Sebastian U. Stich	4
+	Inexact SARAH Algorithm for Stochastic Optimization	2018	Lam M. Nguyen Katya Scheinberg Martin Takáč	4
+	New Convergence Aspects of Stochastic Gradient Algorithms	2019	Lam M. Nguyen Phuong Ha Nguyen Peter Richtárik Katya Scheinberg Martin Takáč Marten van Dijk	4
+ PDF Chat	Accelerated gradient methods for nonconvex nonlinear and stochastic programming	2015	Saeed Ghadimi Guanghui Lan	3
+ PDF Chat	Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications	2018	Antonin Chambolle Matthias J. Ehrhardt Peter Richtárik Carola‐Bibiane Schönlieb	3
+ PDF Chat	A service system with on-demand agent invitations	2015	Guodong Pang Alexander Stolyar	3
+	A method for unconstrained convex minimization problem with the rate of convergence o(1/k^2)	1983	Yurii Nesterov	3
+	Federated Optimization: Distributed Machine Learning for On-Device Intelligence	2016	Jakub Konečný H. Brendan McMahan Daniel Ramage Peter Richtárik	3
+	Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization	2016	Sashank J. Reddi Suvrit Sra Barnabás Póczos Alexander J. Smola	3
+ PDF Chat	Incremental proximal methods for large scale convex optimization	2011	Dimitri P. Bertsekas	3
+ PDF Chat	Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions	2016	Mengdi Wang Ethan X. Fang Han Liu	3
+ PDF Chat	Convergence analysis of distributed stochastic gradient descent with shuffling	2019	Qi Meng Wei Chen Yue Wang Zhi-Ming Ma Tie‐Yan Liu	3
+	Fast Convergence of Stochastic Gradient Descent under a Strong Growth Condition	2013	Mark Schmidt Nicolas Le Roux	3
+ PDF Chat	A Proximal Stochastic Gradient Method with Progressive Variance Reduction	2014	Lin Xiao Tong Zhang	3
+	Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization	2019	Quoc Tran-Dinh Nhan H. Pham Dzung T. Phan Lam M. Nguyen	3
+	SGD without Replacement: Sharper Rates for General Smooth Convex Functions	2019	Anant Raj Prateek Jain Praneeth Netrapalli	3
+	Asynchronous Federated Optimization	2019	Cong Xie Oluwasanmi Koyejo Indranil Gupta	3
+	Principles of mathematical analysis	1964	Walter Rudin	3
+	Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH.	2019	Lam M. Nguyen Marten van Dijk Dzung T. Phan Phuong Ha Nguyen Tsui-Wei Weng Jayant Kalagnanam	3
+	Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter	2017	Zeyuan Allen-Zhu	3
+	Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms	2017	Xiao Han Kashif Rasul Roland Vollgraf	3
+ PDF Chat	Optimization for Machine Learning	2011	Suvrit Sra Sebastian Nowozin Stephen J. Wright	3