b'Lam M. Nguyen'

research

∙ 07/21/2023

Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent

Each round in Differential Private Stochastic Gradient Descent (DPSGD) t...

0 Toan N. Nguyen, et al. ∙

research

∙ 06/11/2023

Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach

Representation learning for time series has been an important research a...

0 Anh Duy Nguyen, et al. ∙

research

∙ 06/01/2023

An End-to-End Time Series Model for Simultaneous Imputation and Forecast

Time series forecasting using historical data has been an interesting an...

0 Trang H. Tran, et al. ∙

research

∙ 04/12/2023

Label-Free Concept Bottleneck Models

Concept bottleneck models (CBM) are a popular way of creating more inter...

0 Tuomas Oikarinen, et al. ∙

research

∙ 02/11/2023

ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction

Deep neural networks (DNN) have shown great capacity of modeling a dynam...

0 Wang Zhang, et al. ∙

research

∙ 12/12/2022

Generalizing DP-SGD with Shuffling and Batching Clipping

Classical differential private DP-SGD implements individual clipping wit...

0 Marten van Dijk, et al. ∙

research

∙ 06/21/2022

Finding Optimal Policy for Queueing Models: New Parameterization

Queueing systems appear in many important real-life applications includi...

0 Trang H. Tran, et al. ∙

research

∙ 06/13/2022

On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms

Stochastic gradient descent (SGD) algorithm is the method of choice in m...

0 Lam M. Nguyen, et al. ∙

research

∙ 02/08/2022

On the Convergence of Gradient Extrapolation Methods for Unbalanced Optimal Transport

We study the Unbalanced Optimal Transport (UOT) between two measures of ...

0 Quang Minh Nguyen, et al. ∙

research

∙ 02/07/2022

Evaluating Robustness of Cooperative MARL: A Model-based Approach

In recent years, a proliferation of methods were developed for cooperati...

7 Nhan H. Pham, et al. ∙

research

∙ 02/07/2022

Nesterov Accelerated Shuffling Gradient Method for Convex Optimization

In this paper, we propose Nesterov Accelerated Shuffling Gradient (NASG)...

0 Trang H. Tran, et al. ∙

research

∙ 02/07/2022

Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution

Deep neural networks (DNNs) have shown great success in many machine lea...

0 Lam M. Nguyen, et al. ∙

research

∙ 12/10/2021

Interpretable Clustering via Multi-Polytope Machines

Clustering is a popular unsupervised learning tool often used to discove...

0 Connor Lawless, et al. ∙

research

∙ 11/11/2021

On the Equivalence between Neural Network and Support Vector Machine

Recent research shows that the dynamics of an infinitely wide neural net...

12 Yilan Chen, et al. ∙

research

∙ 03/05/2021

Federated Learning with Randomized Douglas-Rachford Splitting Methods

In this paper, we develop two new algorithms, called, FedDR and asyncFed...

0 Nhan H. Pham, et al. ∙

research

∙ 02/17/2021

Differential Private Hogwild! over Distributed Local Data Sets

We consider the Hogwild! setting where clients use local SGD iterations ...

0 Marten van Dijk, et al. ∙

research

∙ 11/24/2020

Shuffling Gradient-Based Methods with Momentum

We combine two advanced ideas widely used in optimization for machine le...

0 Trang H. Tran, et al. ∙

research

∙ 11/06/2020

A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees

Several recent publications report advances in training optimal decision...

0 Haoran Zhu, et al. ∙

research

∙ 10/27/2020

Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes

Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where...

0 Marten van Dijk, et al. ∙

research

∙ 08/20/2020

An Optimal Hybrid Variance-Reduced Algorithm for Stochastic Composite Nonconvex Optimization

In this note we propose a new variant of the hybrid variance-reduced pro...

0 Deyi Liu, et al. ∙

research

∙ 07/17/2020

Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise

The feasibility of federated learning is highly constrained by the serve...

0 Marten van Dijk, et al. ∙

research

∙ 06/27/2020

Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems

We develop a novel variance-reduced algorithm to solve a stochastic nonc...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 03/24/2020

Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness

Motivated by broad applications in reinforcement learning and machine le...

0 Thinh T. Doan, et al. ∙

research

∙ 03/01/2020

A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning

We propose a novel hybrid stochastic policy gradient estimator by combin...

0 Nhan H. Pham, et al. ∙

research

∙ 02/19/2020

A Unified Convergence Analysis for Shuffling-Type Gradient Methods

In this paper, we provide a unified convergence analysis for a class of ...

0 Lam M. Nguyen, et al. ∙

research

∙ 02/17/2020

Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization

We develop two new stochastic Gauss-Newton algorithms for solving a clas...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 10/03/2019

BUZz: BUffer Zones for defending adversarial examples in image classification

We propose a novel defense against all existing gradient based adversari...

0 Phuong Ha Nguyen, et al. ∙

research

∙ 07/08/2019

A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization

In this paper, we introduce a new approach to develop stochastic optimiz...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 05/15/2019

Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization

We introduce a hybrid stochastic estimator to design stochastic gradient...

0 Quoc Tran-Dinh, et al. ∙

research

∙ 02/15/2019

ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization

In this paper, we propose a new stochastic algorithmic framework to solv...

0 Nhan H. Pham, et al. ∙

research

∙ 01/22/2019

Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH

The total complexity (measured as the total number of gradient computati...

0 Lam M. Nguyen, et al. ∙

research

∙ 01/22/2019

DTN: A Learning Rate Scheme with Convergence Rate of O(1/t) for SGD

We propose a novel diminishing learning rate scheme, coined Decreasing-T...

0 Lam M. Nguyen, et al. ∙

research

∙ 12/18/2018

PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach

With deep neural networks providing state-of-the-art machine learning mo...

10 Tsui-Wei Weng, et al. ∙

research

∙ 11/25/2018

Inexact SARAH Algorithm for Stochastic Optimization

We develop and analyze a variant of variance reducing stochastic gradien...

0 Lam M. Nguyen, et al. ∙

research

∙ 10/10/2018

Tight Dimension Independent Lower Bound on Optimal Expected Convergence Rate for Diminishing Step Sizes in SGD

We study convergence of Stochastic Gradient Descent (SGD) for strongly c...

0 Phuong Ha Nguyen, et al. ∙

research

∙ 10/09/2018

Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD

We study Stochastic Gradient Descent (SGD) with diminishing step sizes f...

0 Marten van Dijk, et al. ∙

research

∙ 02/11/2018

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

Stochastic gradient descent (SGD) is the optimization algorithm of choic...

0 Lam M. Nguyen, et al. ∙

research

∙ 01/18/2018

When Does Stochastic Gradient Algorithm Work Well?

In this paper, we consider a general stochastic optimization problem whi...

0 Lam M. Nguyen, et al. ∙

research

∙ 05/20/2017

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

In this paper, we study and analyze the mini-batch version of StochAstic...

0 Lam M. Nguyen, et al. ∙

research

∙ 03/01/2017

SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

In this paper, we propose a StochAstic Recursive grAdient algoritHm (SAR...

0 Lam M. Nguyen, et al. ∙

Lam M. Nguyen

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