15 citations · 15 across the 2 of their papers we have counts for
8 papers
Stochastic Gradient Descent: A Nonlinear Time Series Perspective
Jiaqi Li, Zhipeng Lou, Stefan Richter +1
This paper investigates the statistical behavior of Stochastic Gradient Descent (SGD) with constant step size under the framework of iterated random functions. Compared to previous…
A Nonlinear Time-Series Theory for Constant-step SGD with Momentum
Younghun Lee, Jake Zummo, Wei Biao Wu
Constant-step stochastic gradient descent with momentum (SGDM) augments the parameter recursion with a momentum state that carries geometrically decaying memory of past stochastic…
When Does Trust Help? A Leakage-Free Evaluation of Representation- and Aggregation-Based Defenses Against Backdoors in Federated Intrusion Detection
Shiddarth Dey Tusar, Zawad Yalmie Sazid
Federated learning (FL) lets distributed intrusion-detection systems (IDS) train on private traffic, but exposes the global model to poisoning and backdoor attacks under non-IID cl…
Finite-Sample Distribution Theory and Efficient Large-Scale Inference for Online Quantile Regression
Ziyang Wei, Jiaqi Li, Lan Wang +1
This paper studies online quantile regression for large-scale and streaming data using Stochastic SubGradient Descent (SSGD) with constant learning rates. Classical offline inferen…
Moment-Accurate Gaussian Mixtures for Constant-Step Stochastic Approximation
Xiaoli Li, Wei Biao Wu
Local Gaussian models of constant-step learning predict output variability and expected losses, but weak convergence alone does not justify these moment predictions. We establish m…
Central Limit Theorems for Stochastic Gradient Descent Quantile Estimators
Ziyang Wei, Jiaqi Li, Likai Chen +1
This paper develops asymptotic theory for quantile estimation via stochastic gradient descent (SGD) with a constant learning rate. The quantile loss function is neither smooth nor…
Change point analysis with irregular signals
Tobias Kley, Yuhan Philip Liu, Hongyuan Cao +1
This paper considers the problem of testing and estimation of change point where signals after the change point can be highly irregular, which departs from the existing literature…
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach
Ayman Moawad, Ehsan Islam, Namdoo Kim +3
The broader ambition of this article is to popularize an approach for the fair distribution of the quantity of a system's output to its subsystems, while allowing for underlying co…