Bin Hu
Orcid: 0000-0002-5632-7737Affiliations:
- University of Illinois at Urbana-Champaign, Department of Electrical and Computer Engineering, Coordinated Science Laboratory, Champaign, IL, USA
- University of Wisconsin, Wisconsin Institute for Discovery, Madison, WI, USA (2016 - 2018)
- University of Minnesota, Aerospace Engineering and Mechanics Department, Minneapolis, MN, USA (PhD 2016)
According to our database1,
Bin Hu
authored at least 41 papers
between 2013 and 2024.
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Bibliography
2024
Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra.
CoRR, 2024
Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations.
CoRR, 2024
2023
Toward a Theoretical Foundation of Policy Optimization for Learning Control Policies.
Annu. Rev. Control. Robotics Auton. Syst., May, 2023
Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient Method and Global Convergence.
IEEE Trans. Autom. Control., 2023
Connectivity of the Feasible and Sublevel Sets of Dynamic Output Feedback Control With Robustness Constraints.
IEEE Control. Syst. Lett., 2023
Revisiting PGD Attacks for Stability Analysis of High-Dimensional Nonlinear Systems and Perception-Based Control.
IEEE Control. Syst. Lett., 2023
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Complexity of Derivative-Free Policy Optimization for Structured H<sub>∞</sub> Control.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Proceedings of the American Control Conference, 2023
2022
Global Convergence of Direct Policy Search for State-Feedback H<sub>∞</sub> Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential.
CoRR, 2022
Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies.
CoRR, 2022
Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximation.
CoRR, 2022
Revisiting PGD Attacks for Stability Analysis of Large-Scale Nonlinear Systems and Perception-Based Control.
CoRR, 2022
Global Convergence of Direct Policy Search for State-Feedback $\mathcal{H}_\infty$ Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the American Control Conference, 2022
Convex Programs and Lyapunov Functions for Reinforcement Learning: A Unified Perspective on the Analysis of Value-Based Methods.
Proceedings of the American Control Conference, 2022
2021
Policy Optimization for ℋ<sub>2</sub> Linear Control with ℋ<sub>∞</sub> Robustness Guarantee: Implicit Regularization and Global Convergence.
SIAM J. Control. Optim., 2021
Analysis of biased stochastic gradient descent using sequential semidefinite programs.
Math. Program., 2021
Derivative-Free Policy Optimization for Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity.
CoRR, 2021
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
On Imitation Learning of Linear Control Policies: Enforcing Stability and Robustness Constraints via LMI Conditions.
Proceedings of the 2021 American Control Conference, 2021
2020
Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence.
CoRR, 2020
Analytical convergence regions of accelerated gradient descent in nonconvex optimization under Regularity Condition.
Autom., 2020
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems.
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020
Convergence Guarantees of Policy Optimization Methods for Markovian Jump Linear Systems.
Proceedings of the 2020 American Control Conference, 2020
2018
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs.
Proceedings of the 35th International Conference on Machine Learning, 2018
Proceedings of the 2018 Annual American Control Conference, 2018
2017
Proceedings of the 34th International Conference on Machine Learning, 2017
A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints.
Proceedings of the 30th Conference on Learning Theory, 2017
Proceedings of the 2017 American Control Conference, 2017
Proceedings of the 55th Annual Allerton Conference on Communication, 2017
2016
Exponential Decay Rate Conditions for Uncertain Linear Systems Using Integral Quadratic Constraints.
IEEE Trans. Autom. Control., 2016
2015
Pivotal decomposition for reliability analysis of fault tolerant control systems on unmanned aerial vehicles.
Reliab. Eng. Syst. Saf., 2015
Int. J. Appl. Math. Comput. Sci., 2015
2014
Proceedings of the American Control Conference, 2014
2013
Proceedings of the 51st Annual Allerton Conference on Communication, 2013