| Smart machining process using machine learning: A review and perspective on machining industry DH Kim, TJY Kim, X Wang, M Kim, YJ Quan, JW Oh, SH Min, H Kim, ... International Journal of Precision Engineering and Manufacturing-Green …, 2018 | 289 | 2018 |
| Wasserstein distributionally robust stochastic control: A data-driven approach I Yang IEEE Transactions on Automatic Control 66 (8), 3863-3870, 2021 | 117 | 2021 |
| Micro ECM with ultrasonic vibrations using a semi-cylindrical tool I Yang, MS Park, CN Chu International Journal of Precision Engineering and Manufacturing 10, 5-10, 2009 | 94 | 2009 |
| Risk-Aware Motion Planning and Control Using CVaR-Constrained Optimization A Hakobyan, GC Kim, I Yang IEEE Robotics and Automation Letters 4 (4), 3924-3931, 2019 | 86 | 2019 |
| A Convex Optimization Approach to Distributionally Robust Markov Decision Processes with Wasserstein Distance I Yang IEEE Control Systems Letters 1 (1), 164-169, 2017 | 83 | 2017 |
| Optimal control of conditional value-at-risk in continuous time CW Miller, I Yang SIAM Journal on Control and Optimization 55 (2), 856-884, 2017 | 70 | 2017 |
| Appropriate smart factory for SMEs: concept, application and perspective WK Jung, DR Kim, H Lee, TH Lee, I Yang, BD Youn, D Zontar, ... International Journal of Precision Engineering and Manufacturing 22, 201-215, 2021 | 63 | 2021 |
| Safety-Aware Optimal Control of Stochastic Systems Using Conditional Value-at-Risk S Samuelson, I Yang American Control Conference (ACC), 2018, 6285-6290, 2018 | 57 | 2018 |
| A dynamic game approach to distributionally robust safety specifications for stochastic systems I Yang Automatica 94, 94-101, 2018 | 54 | 2018 |
| Wasserstein Distributionally Robust Motion Control for Collision Avoidance Using Conditional Value-at-Risk A Hakobyan, I Yang IEEE Transactions on Robotics, 2021 | 51 | 2021 |
| Sample efficient home power anomaly detection in real time using semi-supervised learning X Wang, I Yang, SH Ahn IEEE Access 7, 139712-139725, 2019 | 46 | 2019 |
| Risk-Sensitive Safety Analysis Using Conditional Value-at-Risk MP Chapman, R Bonalli, KM Smith, I Yang, M Pavone, CJ Tomlin IEEE Transactions on Automatic Control 67 (12), 6521 - 6536, 2022 | 45* | 2022 |
| Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls J Kim, J Shin, I Yang Journal of Machine Learning Research 22 (206), 1-34, 2021 | 27 | 2021 |
| Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach S Huh, I Yang arXiv preprint arXiv:2002.10126, 2020 | 25 | 2020 |
| Indirect load control for electricity market risk management via risk-limiting dynamic contracts I Yang, DS Callaway, CJ Tomlin 2015 American Control Conference (ACC), 3025-3031, 2015 | 24 | 2015 |
| Wasserstein distributionally robust motion planning and control with safety constraints using conditional value-at-risk A Hakobyan, I Yang 2020 IEEE International Conference on Robotics and Automation (ICRA), 490-496, 2020 | 23 | 2020 |
| Improved regret analysis for variance-adaptive linear bandits and horizon-free linear mixture MDPs Y Kim, I Yang, KS Jun Advances in Neural Information Processing Systems 35, 1060-1072, 2022 | 22 | 2022 |
| Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time J Kim, I Yang Learning for Dynamics and Control, 739-748, 2020 | 22 | 2020 |
| Submodularity of energy storage placement in power networks J Qin, I Yang, R Rajagopal 2016 IEEE 55th Conference on Decision and Control (CDC), 686-693, 2016 | 20 | 2016 |
| Accelerated gradient methods for geodesically convex optimization: Tractable algorithms and convergence analysis J Kim, I Yang International Conference on Machine Learning, 11255-11282, 2022 | 18* | 2022 |
Claire TomlinUC BerkeleyVerified email at berkeley.edu
