2025
Egocentric Conformal Prediction for Safe and Efficient Navigation in Dynamic Cluttered Environments.
CoRR, April, 2025
Robust Continuous-Time Generation Scheduling under Power Demand Uncertainty: An Affine Decision Rule Approach.
CoRR, April, 2025
On the Steady-State Distributionally Robust Kalman Filter.
CoRR, March, 2025
2024
Control of Fab Lifters via Deep Reinforcement Learning: A Semi-MDP Approach.
IEEE Trans Autom. Sci. Eng., October, 2024
Wasserstein Distributionally Robust Control of Partially Observable Linear Stochastic Systems.
IEEE Trans. Autom. Control., September, 2024
Risk-Aware Wasserstein Distributionally Robust Control of Vessels in Natural Waterways.
IEEE Trans. Control. Syst. Technol., July, 2024
Multiparametric Analysis of Multi-Task Markov Decision Processes: Structure, Invariance, and Reducibility.
IEEE Control. Syst. Lett., 2024
Wasserstein Distributionally Robust Regret Minimization.
IEEE Control. Syst. Lett., 2024
Generalized Continuous-Time Models for Nesterov's Accelerated Gradient Methods.
CoRR, 2024
Wasserstein Distributionally Robust Control and State Estimation for Partially Observable Linear Systems.
CoRR, 2024
Approximate Thompson Sampling for Learning Linear Quadratic Regulators with O(√T) Regret.
CoRR, 2024
Anderson acceleration for partially observable Markov decision processes: A maximum entropy approach.
Autom., 2024
On task-relevant loss functions in meta-reinforcement learning.
Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024
2023
Distributional Robustness in Minimax Linear Quadratic Control with Wasserstein Distance.
SIAM J. Control. Optim., April, 2023
Distributionally Robust Risk Map for Learning-Based Motion Planning and Control: A Semidefinite Programming Approach.
IEEE Trans. Robotics, February, 2023
Maximum Entropy Optimal Control of Continuous-Time Dynamical Systems.
IEEE Trans. Autom. Control., 2023
Distributionally Robust Differential Dynamic Programming With Wasserstein Distance.
IEEE Control. Syst. Lett., 2023
On Task-Relevant Loss Functions in Meta-Reinforcement Learning and Online LQR.
CoRR, 2023
Using affine policies to reformulate two-stage Wasserstein distributionally robust linear programs to be independent of sample size.
CoRR, 2023
Convergence analysis of ODE models for accelerated first-order methods via positive semidefinite kernels.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Distributionally Robust Optimization with Unscented Transform for Learning-Based Motion Control in Dynamic Environments.
Proceedings of the IEEE International Conference on Robotics and Automation, 2023
Unifying Nesterov's Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions.
Proceedings of the International Conference on Machine Learning, 2023
On Concentration Bounds for Bayesian Identification of Linear Non-Gaussian Systems.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023
Data-Driven Stochastic Optimization Using Upper Confidence Bounds: Performance Guarantees and Distributional Robustness.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023
2022
Wasserstein Distributionally Robust Motion Control for Collision Avoidance Using Conditional Value-at-Risk.
IEEE Trans. Robotics, 2022
Risk-Sensitive Safety Analysis Using Conditional Value-at-Risk.
IEEE Trans. Autom. Control., 2022
Infusing Model Predictive Control Into Meta-Reinforcement Learning for Mobile Robots in Dynamic Environments.
IEEE Robotics Autom. Lett., 2022
On representation formulas for optimal control: A Lagrangian perspective.
CoRR, 2022
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis.
Proceedings of the International Conference on Machine Learning, 2022
Wasserstein Distributionally Robust Control of Partially Observable Linear Systems: Tractable Approximation and Performance Guarantee.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022
On Affine Policies for Wasserstein Distributionally Robust Unit Commitment.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022
2021
Wasserstein Distributionally Robust Stochastic Control: A Data-Driven Approach.
IEEE Trans. Autom. Control., 2021
Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls.
J. Mach. Learn. Res., 2021
Training Wasserstein GANs without gradient penalties.
CoRR, 2021
Toward Improving the Distributional Robustness of Risk-Aware Controllers in Learning-Enabled Environments.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021
On Anderson Acceleration for Partially Observable Markov Decision Processes.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021
2020
A Convex Optimization Approach to Dynamic Programming in Continuous State and Action Spaces.
J. Optim. Theory Appl., 2020
Hamilton-Jacobi-Bellman Equations for Maximum Entropy Optimal Control.
CoRR, 2020
Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach.
CoRR, 2020
Multi-Objective Predictive Taxi Dispatch via Network Flow Optimization.
IEEE Access, 2020
A3DQN: Adaptive Anderson Acceleration for Deep Q-Networks.
Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020
Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time.
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020
Learning-Based Distributionally Robust Motion Control with Gaussian Processes.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020
STAR: Spatio-Temporal Prediction of Air Quality Using a Multimodal Approach.
Proceedings of the Intelligent Systems and Applications, 2020
Wasserstein Distributionally Robust Motion Planning and Control with Safety Constraints Using Conditional Value-at-Risk.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020
Minimax Control of Ambiguous Linear Stochastic Systems Using the Wasserstein Metric.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020
A Stochastic Consensus Method for Nonconvex Optimization on the Stiefel Manifold.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020
2019
Submodularity of Storage Placement Optimization in Power Networks.
IEEE Trans. Autom. Control., 2019
Risk-Aware Motion Planning and Control Using CVaR-Constrained Optimization.
IEEE Robotics Autom. Lett., 2019
Sample Efficient Home Power Anomaly Detection in Real Time Using Semi-Supervised Learning.
IEEE Access, 2019
On Improving the Robustness of Reinforcement Learning-based Controllers using Disturbance Observer.
Proceedings of the 58th IEEE Conference on Decision and Control, 2019
Stochastic Subgradient Methods for Dynamic Programming in Continuous State and Action Spaces.
Proceedings of the 58th IEEE Conference on Decision and Control, 2019
2018
A dynamic game approach to distributionally robust safety specifications for stochastic systems.
Autom., 2018
Safety-Aware Optimal Control of Stochastic Systems Using Conditional Value-at-Risk.
Proceedings of the 2018 Annual American Control Conference, 2018
2017
Variance-Constrained Risk Sharing in Stochastic Systems.
IEEE Trans. Autom. Control., 2017
Optimal Control of Conditional Value-at-Risk in Continuous Time.
SIAM J. Control. Optim., 2017
A Convex Optimization Approach to Distributionally Robust Markov Decision Processes With Wasserstein Distance.
IEEE Control. Syst. Lett., 2017
Distributionally robust stochastic control with conic confidence sets.
Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017
Data-driven distributionally robust control of energy storage to manage wind power fluctuations.
Proceedings of the IEEE Conference on Control Technology and Applications, 2017
2016
Approximation Algorithms for Optimization of Combinatorial Dynamical Systems.
IEEE Trans. Autom. Control., 2016
Control of Supermarket Refrigeration Systems via Online Combinatorial Optimization.
CoRR, 2016
Submodularity of energy storage placement in power networks.
Proceedings of the 55th IEEE Conference on Decision and Control, 2016
Reducing electricity price volatility via stochastic storage control.
Proceedings of the 2016 American Control Conference, 2016
2015
Optimal Dynamic Contracts for a Large-Scale Principal-Agent Hierarchy: A Concavity-Preserving Approach.
CoRR, 2015
Indirect load control for electricity market risk management via risk-limiting dynamic contracts.
Proceedings of the American Control Conference, 2015
2014
Reaction-diffusion systems in protein networks: Global existence and identification.
Syst. Control. Lett., 2014
Utility learning model predictive control for personal electric loads.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014
Dynamic contracts with partial observations: Application to indirect load control.
Proceedings of the American Control Conference, 2014
Direct load control for electricity market risk management via risk-limiting dynamic contracts.
Proceedings of the 52nd Annual Allerton Conference on Communication, 2014
2013
One-shot computation of reachable sets for differential games.
Proceedings of the 16th international conference on Hybrid systems: computation and control, 2013
Regularization-based identification for level set equations.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013
Infinitesimal interconnection variation in nonlinear networked systems.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013
Identification of surface tension in mean curvature flow.
Proceedings of the American Control Conference, 2013