Marten van Dijk | DeepAI

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∙ 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. ∙

∙ 12/19/2022

Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysis

In this book chapter, we briefly describe the main components that const...

0 Quoc Tran-Dinh, 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. ∙

∙ 11/26/2022

Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning

Recent advances in adversarial machine learning have shown that defenses...

0 Ethan Rathbun, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 09/29/2021

Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks

The field of adversarial machine learning has experienced a near exponen...

0 Kaleel Mahmood, 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. ∙

∙ 05/01/2019

On the Convergence Rates of Learning-based Signature Generation Schemes to Contain Self-propagating Malware

In this paper, we investigate the importance of a defense system's learn...

0 Saeed Valizadeh, 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. ∙

∙ 01/06/2019

Toward a Theory of Cyber Attacks

We provide a general methodology for analyzing defender-attacker based "...

0 Saeed Valizadeh, 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. ∙

∙ 07/29/2018

TREVERSE: Trial-and-Error Lightweight Secure Reverse Authentication with Simulatable PUFs

A physical unclonable function (PUF) generates hardware intrinsic volati...

0 Yansong Gao, et al. ∙

∙ 04/13/2018

Comments on "Defeating HaTCh: Building Malicious IP Cores"

Recently, Haider et al. introduced the first rigorous hardware Trojan de...

0 Syed Kamran Haider, 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. ∙

∙ 11/04/2016

Flat ORAM: A Simplified Write-Only Oblivious RAM Construction for Secure Processors

Oblivious RAM (ORAM) is a cryptographic primitive which obfuscates the a...

0 Syed Kamran Haider, et al. ∙

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