Phuong Ha Nguyen | DeepAI

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Featured Co-authors

Peter Richtarik
143 publications
Martin Takáč
53 publications
Lam M. Nguyen
40 publications
Quoc Tran-Dinh
34 publications
Tsui-Wei Weng
25 publications
Marten van Dijk
25 publications
Katya Scheinberg
22 publications
Thanh Nguyen
20 publications
Dzung T. Phan
12 publications
Kaleel Mahmood
9 publications
Nhan H. Pham
8 publications

∙ 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. ∙

∙ 03/08/2023

Considerations on the Theory of Training Models with Differential Privacy

In federated learning collaborative learning takes place by a set of cli...

0 Marten van Dijk, et al. ∙

∙ 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. ∙

∙ 01/05/2022

Secure Remote Attestation with Strong Key Insulation Guarantees

Recent years have witnessed a trend of secure processor design in both a...

0 Deniz Gurevin, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 06/18/2020

Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples

Recent defenses published at venues like NIPS, ICML, ICLR and CVPR are m...

0 Kaleel Mahmood, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

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