{"id":11184,"date":"2023-01-12T19:48:53","date_gmt":"2023-01-12T18:48:53","guid":{"rendered":"https:\/\/www.digitalfutures.kth.se\/?page_id=11184"},"modified":"2023-01-17T13:47:41","modified_gmt":"2023-01-17T12:47:41","slug":"publications","status":"publish","type":"page","link":"https:\/\/wpmu-tris.sys.kth.se\/digitalfutures\/research\/collaborative-projects\/completed-collaborative-projects\/data-limited-learning-of-complex-dynamical-systems\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

We like to inspire and share interesting knowledge!<\/strong><\/p>\n

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  1. Filippo Vannella, Jaeseong Jeong, and Alexandre Proutiere. Off-Policy Learning in Contextual Bandits for Remote Electrical Tilt Optimization. IEEE transactions on Vehicular Technology, 2022.<\/li>\n
  2. Filippo Vannella, Alexandre Proutiere, Yassir Jedra, and Jaeseong Jeong. Remote Electrical Tilt Optimization: a contextual bandit approach. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM) 2022.<\/li>\n
  3. Daniel Lunde\u0301n, Joey O\u0308hman, Jan Kudlicka, Viktor Senderov, Fredrik Ronquist, and David Broman. Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference. In the Proceedings of 31st European Symposium on Programming (ESOP), 2022.<\/li>\n
  4. Ke\u0301vin Colin, Mina Fe\u0301rizbegovic, and Ha\u030akan Hjalmarsson, Regret Minimization for Linear Quadratic Adaptive Controllers using Fisher Feedback Exploration, IEEE Control Systems Letters and In Proceedings of the 61th IEEE Conference on Decision and Control (CDC), 2022.<\/li>\n
  5. Yassir Jedra and Alexandre Proutiere. Minimal expected regret in online LQR. In Proceedings of the International Conference on Artificial Intelligence and Statistics(AISTATS), 2022.<\/li>\n
  6. Robert Bereza, Oscar Eriksson, Mohamed R.-H. Abdalmoaty, David Broman andHa\u030akan Hjalmarsson. Stochastic Approximation for Identification of Non-Linear Differential-Algebraic Equations with Process Disturbances, In Proceedings of the IEEE Conference on Decision and Control (CDC), 2022.<\/li>\n
  7. A Ghosh, A.E. Fontcurbeta, Mohamed R. Abdalmoaty, Saikat Chatterjee, Time-varying Normalizing Flow for Generative Modeling of Dynamical Signals, European Signal Processing Conference (EUSIPCO), 2022.<\/li>\n
  8. S.Das, A.M. Javid, P.B. Gohain, Y.C. Eldar, S. Chatterjee. Neural Greedy Pursuit for Feature Selection, In the Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE WCCI 2022.<\/li>\n
  9. X Liang, AM Javid, M Skoglund, S Chatterjee, Decentralized learning of randomization-based neural networks with centralized equivalence, AppliedSoft Computing, 2022.<\/li>\n
  10. David Broman. Interactive Programmatic Modeling. In ACM Transactions on Embedded Computing Systems (TECS), Volume 20, Issue 4, Article No 33, Pages 1-26, ACM, 2021.<\/li>\n
  11. Mohamed R.-H. Abdalmoaty, Oscar Eriksson, Robert Bereza, David Broman and Ha\u030akan Hjalmarsson. Identification of Non-Linear Differential-Algebraic Equation Models with Process Disturbances, In Proceedings of the IEEE Conference on Decision and Control (CDC), 2021.<\/li>\n
  12. Mina Ferizbegovic, Per Mattsson, Thomas Scho\u0308n, and H. Hjalmarsson. Bayes Control of Hammerstein Systems, 19th IFAC Symposium on System Identification, 2021.<\/li>\n
  13. Daniel Lunde\u0301n, Johannes Borgstro\u0308m, and David Broman. Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages. In Proceedings of 30th European Symposium on Programming (ESOP), LNCS vol. 12648, Springer, 2021.<\/li>\n
  14. Viktor Palmkvist, Elias Castegren, Philipp Haller, and David Broman. Resolvable Ambiguity: Principled Resolution of Syntactically Ambiguous Programs. In Proceedings of the 30th ACM SIGPLAN International Conference on Compiler Construction (CC), ACM, 2021.<\/li>\n
  15. A.M. Javid, S. Das, M. Skoglund and S. Chatterjee, A ReLU Dense Layer to Improve the Performance of Neural Networks, in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.<\/li>\n
  16. X. Liang, M. Skoglund, and S. Chatterjee, Feature reuse for a randomization based neural network, in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.<\/li>\n
  17. P.G. Jurado X. Liang, A.M. Javid, and S. Chatterjee, Use of deterministic transforms to design weight matrices of a neural network, in European Signal Processing Conference (EUSIPCO), 2021.<\/li>\n
  18. Di\u0301ogo Rodrigues, Mohamed R. Abdalmoaty, Elling W. Jacobsen, Ve\u0301ronqiue Chotteau, and Ha\u030akan Hjalmarsson. An Integrated Approach for Modeling and Identification of Perfusion Bioreactors via Basis Flux Modes, Computers & Chemical Engineering, 2021.<\/li>\n
  19. Aymen Al Marjani and Alexandre Proutiere. Adaptive Sampling for Best Policy Identification in Markov Decision Processes. In Proceedings of International Conference on Machine Learning (ICML), 2021.<\/li>\n
  20. Aymen Al Marjani, Aure\u0301lien Garivier, and Alexandre Proutiere: Navigating to the Best Policy in Markov Decision Processes. In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), 2021.<\/li>\n
  21. Damianos Tranos and Alexandre Proutiere: Regret Analysis in Deterministic Reinforcement Learning. In Proceedings of the IEEE Conference on Decision and Control (CDC), 2021.<\/li>\n
  22. Zhang, L., Wang, M., Castan, A., Hjalmarsson, H. and Chotteau, V., Probabilistic model by Bayesian network for the prediction of antibody glycosylation in perfusion and fed\u2010batch cell cultures. Biotechnology and Bioengineering, 118(9), pp.3447-3459, 2021.<\/li>\n
  23. Chotteau, V., Hagrot, E., Zhang, L., Ma\u0308kinen, M. Mathematical modelling of cell culture processes. In Cell Culture Engineering and Technology (pp. 467-484). Springer, Cham, 2021.<\/li>\n
  24. Mingliang Wang, Riccardo S. Risuleo, Elling W. Jacobsen, Ve\u0301ronique Chotteau and Ha\u030akan Hjalmarsson. Identification of Nonlinear Kinetics of Macroscopic Bio-reactions Using Multilinear Gaussian Processes, Computers and Chemical Engineering, 123(2), 2020.<\/li>\n
  25. Stefanie Fonken, Mina Ferizbegovic, and H. Hjalmarsson. Consistent Identification of Dynamic Networks Subject to White Noise Using Weighted Null-Space Fitting, In Proceedings of the IFAC 2020 World Congress, 2020.<\/li>\n
  26. Riccardo S. Risuleo and Ha\u030akan Hjalmarsson. Nonparametric Models for Hammerstein-Wiener and Wiener-Hammerstein System Identification, In Proceedings of the IFAC 2020 World Congress, 2020.<\/li>\n
  27. Saranya Natarajan and David Broman. Temporal Property-Based Testing of a Timed C Compiler using Time-Flow Graph Semantics. In the Proceedings of the Forum on specification & Design Languages (FDL 2020), IEEE, 2020.<\/li>\n
  28. Mohamed R. Abdalmoaty and Ha\u030akan Hjalmarsson. Identification of Stochastic Nonlinear Models Using Optimal Estimating Functions, Automatica, 119, 2020. Mina Ferizbegovic, Jack Umenberger, Ha\u030akan Hjalmarsson and Thomas Scho\u0308n. Learning robust LQ-controllers using application oriented exploration, IEEE Control Systems Letters, 4(4):19-24 and jointly published in the Proceedings of the IEEE Conference on Decision and Control (CDC), 2020.<\/li>\n
  29. A.M. Javid, A. Venkitaraman, M. Skoglund, S. Chatterjee, High-dimensional neural feature design for layer-wise reduction of training cost, EURASIP Journal on Advances in Signal Processing, 2020.<\/li>\n
  30. Yassir Jedra and Alexandre Proutiere. Finite-time Identification of Stable Linear Systems Optimality of the Least-Squares Estimator. In Proceedings of the IEEE Conference on Decision and Control (CDC), 2020.<\/li>\n
  31. Filippo Vannella, Jaeseong Jeong, and Alexandre Proutiere. Off-policy Learning for Remote Electrical Tilt Optimization. VTC-Fall, 2020.<\/li>\n
  32. Jack Umenberger, Mina Ferizbegovic, Thomas Scho\u0308n and Ha\u030akan Hjalmarsson. Robust exploration in linear quadratic reinforcement learning, In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), 2019 (Spotlight paper).<\/li>\n
  33. Yassir Jedra, Alexandre Proutiere. Sample Complexity Lower Bounds for Linear System Identification. In Proceedings of the IEEE Conference on Decision and Control (CDC), 2019.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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