| On online optimization: Dynamic regret analysis of strongly convex and smooth problems TJ Chang, S Shahrampour Proceedings of the AAAI conference on artificial intelligence 35 (8), 6966-6973, 2021 | 20 | 2021 |
| Efficient two-step adversarial defense for deep neural networks TJ Chang, Y He, P Li arXiv preprint arXiv:1810.03739, 2018 | 18 | 2018 |
| Distributed online linear quadratic control for linear time-invariant systems TJ Chang, S Shahrampour 2021 American Control Conference (ACC), 923-928, 2021 | 15 | 2021 |
| Regret analysis of distributed online LQR control for unknown LTI systems TJ Chang, S Shahrampour IEEE Transactions on Automatic Control 69 (1), 667-673, 2023 | 10 | 2023 |
| 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 | 7 | 2023 |
| Unconstrained online optimization: Dynamic regret analysis of strongly convex and smooth problems TJ Chang, S Shahrampour arXiv preprint arXiv:2006.03912, 2020 | 4 | 2020 |
| 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 | 3 | 2022 |
| 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 | 2 | 2022 |
| Regret Analysis of Policy Optimization over Submanifolds for Linearly Constrained Online LQG TJ Chang, S Shahrampour arXiv preprint arXiv:2403.08553, 2024 | 1 | 2024 |
| 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 | 1 | 2020 |
| 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 |
