PAC-Bayesian theory for stochastic LTI systems. - Deividas Eringis; John Leth; Zheng-Hua Tan; Rafal Wisniewski; Alireza Fakhrizadeh Esfahani; Mihály Petreczky (CDC, 2021)

Optimal Prediction of Unmeasured Output from Measurable Outputs In LTI Systems. - Deividas Eringis; John Leth; Zheng-Hua Tan; Rafal Wisniewski; Mihály Petreczky (CoRR, 2021)

Learning-Based Predictive Control with Gaussian Processes: An Application to Urban Drainage Networks. - Krisztian Mark Balla; Deividas Eringis; Mohamad Al Ahdab; Jan Dimon Bendtsen; Carsten Skovmose Kallesøe; Carlos Ocampo-Martinez (ACC, 2022)

PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models. - Deividas Eringis; John Leth; Zheng-Hua Tan; Rafal Wisniewski; Mihály Petreczky (CoRR, 2022)

Explicit construction of the minimum error variance estimator for stochastic LTI-ss systems. - Deividas Eringis; John Leth; Zheng-Hua Tan; Rafal Wisniewski; Mihály Petreczky (Autom., 2023)

PAC-Bayesian bounds for learning LTI-ss systems with input from empirical loss. - Deividas Eringis; John Leth; Zheng-Hua Tan; Rafael Wisniewski; Mihály Petreczky (CoRR, 2023)

PAC-Bayes Generalisation Bounds for Dynamical Systems including Stable RNNs. - Deividas Eringis; John Leth; Zheng-Hua Tan; Rafael Wisniewski; Mihály Petreczky (AAAI, 2024)

PAC-Bayesian Error Bound, via Rényi Divergence, for a Class of Linear Time-Invariant State-Space Models. - Deividas Eringis; John Leth; Zheng-Hua Tan; Rafal Wisniewski; Mihály Petreczky (ICML, 2024)