Seguir
Ting-Jui Chang
Ting-Jui Chang
Northeastern University
Dirección de correo verificada de northeastern.edu - Página principal
Título
Citado por
Citado por
Año
On Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems
TJ Chang, S Shahrampour
Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021
22*2021
Efficient two-step adversarial defense for deep neural networks
TJ Chang, Y He, P Li
arXiv preprint arXiv:1810.03739, 2018
162018
Distributed online linear quadratic control for linear time-invariant systems
TJ Chang, S Shahrampour
2021 American Control Conference (ACC), 923-928, 2021
152021
Regret analysis of distributed online LQR control for unknown LTI systems
TJ Chang, S Shahrampour
IEEE Transactions on Automatic Control, 2023
72023
Dynamic regret analysis of safe distributed online optimization for convex and non-convex problems
TJ Chang, S Chaudhary, D Kalathil, S Shahrampour
arXiv preprint arXiv:2302.12320, 2023
32023
Regret Analysis of Policy Optimization over Submanifolds for Linearly Constrained Online LQG
TJ Chang, S Shahrampour
arXiv preprint arXiv:2403.08553, 2024
12024
Distributed online system identification for lti systems using reverse experience replay
TJ Chang, S Shahrampour
2022 IEEE 61st Conference on Decision and Control (CDC), 6672-6677, 2022
12022
RFN: A Random-Feature Based Newton Method for Empirical Risk Minimization in Reproducing Kernel Hilbert Spaces
TJ Chang, S Shahrampour
IEEE Transactions on Signal Processing 70, 5308-5319, 2022
12022
Global convergence of Newton method for empirical risk minimization in reproducing kernel hilbert space
TJ Chang, S Shahrampour
2020 54th Asilomar Conference on Signals, Systems, and Computers, 1222-1226, 2020
12020
Regret Analysis of Distributed Online Control for LTI Systems with Adversarial Disturbances
TJ Chang, S Shahrampour
arXiv preprint arXiv:2310.03206, 2023
2023
A Random-Feature Based Newton Method for Empirical Risk Minimization in Reproducing Kernel Hilbert Space
S Shahrampour, TJ Chang
arXiv, arXiv: 2002.04753, 2020
2020
Enhancing Resilience Against Adversarial Attacks of Deep Neural Networks Using Efficient Two-Step Adversarial Defense
TJ Chang
2018
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–12