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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...
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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...
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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...
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12/12/2022
Generalizing DP-SGD with Shuffling and Batching Clipping
Classical differential private DP-SGD implements individual clipping wit...
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11/26/2022
Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning
Recent advances in adversarial machine learning have shown that defenses...
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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...
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01/05/2022
Secure Remote Attestation with Strong Key Insulation Guarantees
Recent years have witnessed a trend of secure processor design in both a...
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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...
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02/17/2021
Differential Private Hogwild! over Distributed Local Data Sets
We consider the Hogwild! setting where clients use local SGD iterations ...
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10/27/2020
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes
Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where...
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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...
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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...
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03/01/2020
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
We propose a novel hybrid stochastic policy gradient estimator by combin...
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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 ...
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10/03/2019
BUZz: BUffer Zones for defending adversarial examples in image classification
We propose a novel defense against all existing gradient based adversari...
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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...
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01/22/2019
Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH
The total complexity (measured as the total number of gradient computati...
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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...
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01/06/2019
Toward a Theory of Cyber Attacks
We provide a general methodology for analyzing defender-attacker based "...
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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...
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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...
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07/29/2018
TREVERSE: Trial-and-Error Lightweight Secure Reverse Authentication with Simulatable PUFs
A physical unclonable function (PUF) generates hardware intrinsic volati...
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04/13/2018
Comments on "Defeating HaTCh: Building Malicious IP Cores"
Recently, Haider et al. introduced the first rigorous hardware Trojan de...
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02/11/2018
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Stochastic gradient descent (SGD) is the optimization algorithm of choic...
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11/04/2016
