Risk-aware Wasserstein distributionally robust control of vessels in natural waterways – IEEE Transactions on Control Systems Technology, 32(4):1471–1478, 2024
Juan Moreno Nadales, Astghik Hakobyan, David Muñoz de la Peña, Daniel Limon, Insoon Yang
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Wasserstein distributionally robust control of partially observable linear stochastic systems – IEEE Transactions on Automatic Control, 69(9):6121–6136, 2024
Astghik Hakobyan, Insoon Yang
Wasserstein distributionally robust regret minimization – IEEE Control Systems Letters, 8:820–825, 2024 (Selected for CDC ’24)
Youngchae Cho, Insoon Yang
Multiparametric analysis of multi-task Markov decision processes: structure, invariance, and reducibility – IEEE Control Systems Letters, 8:928–933, 2024 (Selected for CDC ’24)
Jaeuk Shin, Insoon Yang
On task-relevant loss functions in meta-reinforcement learning – Learning for Dynamics and Control (L4DC), 2024
Jaeuk Shin, Giho Kim, Howon Lee, Joonho Han, Insoon Yang
Anderson acceleration for partially observable Markov decision processes: a maximum entropy approach – Automatica, 163:111557, 2024
Mingyu Park, Jaeuk Shin, Insoon Yang
Control of fab lifters via deep reinforcement learning: a semi-MDP approach – IEEE Transactions on Automation Science and Engineering, accepted
Giho Kim, Jaeuk Shin, Gihun Kim, Joonrak Kim, Insoon Yang
Convergence analysis of ODE models for accelerated first-order methods via positive semidefinite kernels – Advances in Neural Information Processing Systems (NeurIPS), 2023
Jungbin Kim, Insoon Yang
[NeurIPS] Analysis of ODE models for accelerated methods via positive semidefinite kernels – Advances in Neural Information Processing Systems (NeurIPS), 2023
Jungbin Kim, Insoon Yang
Distributionally robust differential dynamic programming with Wasserstein distance – IEEE Control Systems Letters, 7:2329–2334, 2023 (Selected for CDC ’23)
Astghik Hakobyan, Insoon Yang