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Publications

We like to inspire and share interesting knowledge!

Journal articles (J)

  1. R.A.González,C.R.Rojas,S.Pan,andJ.Welsh.“Theoreticalandpracticalaspects of the convergence of the SRIVC estimator for over-parameterized models”. Automatica, 142:110355, 2022
  2. S. Pan, J. Welsh, R. A. González, and C. R. Rojas. “Consistency analysis and bias elimination of the instrumental variable based state variable filter method”. Automatica, 144:110511, 2022
  3. P. Wachel and C. R. Rojas. “An adversarial approach to adaptive model predictive control”. Journal of Advances in Applied & Computational Mathematics, 9, 135–146, 2022
  4. R. A. González, C. R. Rojas, S. Pan, and J. Welsh. “Refined instrumental variable methods for unstable continuous-time systems in closed-loop”. International Journal of Control (accepted for publication), 2022
  5. C.R.RojasandP.Wachel.“Onstate-spacerepresentationsofgeneraldiscrete-time dynamical systems”. IEEE Transactions on Automatic Control (accepted for publication), 2022
  6. R.Mochaourab,A.Venkitaraman,I.Samsten,P.Papapetrou,andC.R.Rojas,“Post- hoc Explainability for Time Series Classification: Toward a Signal Processing Perspective,” IEEE Signal Processing Magazine, Special Issue on Explainability in Data Science: Interpretability, Reproducibility, and Replicability, vol. 39, no. 4, Jul. 2022
  7. S. Greenstein, P. Papapetrou, and R. Mochaourab, “Embedding Human Values into Artificial Intelligence,” in De Vries, Katja (ed.), De Lege, Iustus förlag, Uppsala University, 2022
  8. J. Rebane, I. Samsten, L. Bornemann, and P. Papapetrou, “SMILE: A feature-based temporal abstraction framework for event-interval sequence classification”. In Data Mining and Knowledge Discovery (DAMI) 35(1): 372-399, 2021
  9. B. Djehiche, O. Mazhar, and C. R. Rojas. “Finite impulse response models: A nonasymptotic analysis of the least squares estimator”. Bernoulli, 27(2):976–1000, 2021
  10. R. A. González, C. R. Rojas, S. Pan, and J. Welsh. “Consistent identification of continuous-time systems under multisine input signal excitation”. Automatica, 133:109859, 2021
  11. I. Lourenço, R. Mattila, C. R. Rojas, X. Hu, and B.Wahlberg. “Hidden Markov models: Inverse filtering, belief estimation and privacy protection”. Journal of Systems Science and Complexity, 34:1801–1820, 2021
  12. J. Rebane, I. Samsten, and P. Papapetrou, “Exploiting Complex Medical Data with Interpretable Deep Learning for Adverse Drug Event Prediction”. In Artificial Intelligence in Medicine (AIM), 28(8): 1651-1659, 2021
  13. Locally and globally explainable time series tweaking KAIS), 62 (5): 1671- 1700, 2020
  14. R. Mattila, C. R. Rojas, V. Krishnamurthy, and B. Wahlberg. “Inverse filtering for hidden Markov models with applications to counter-adversarial autonomous systems”. IEEE Transactions on Signal Processing, 68:4987–5002, 2020

Conference papers (C)

  1. Z. Lee, M. Trincavelli, and P. Papapetrou, “Finding Local Groupings of Time Series“, In the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML/PKDD), to appear
  2. L. Mondrejevski, I. Miliou, A. Montanino, D. Pitts, J. Hollmén, and P. Papapetrou, “FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction”. In Computer-Based Medical Systems (CBMS), to appear
  3. M. Bampa, T. Fasth, S. Magnusson, and P. Papapetrou, “EpidRLearn: Learning intervention strategies for epidemics with Reinforcement Learning“. In the International Conference on Artificial Intelligence in Medicine (AIME), 2022
  4. S. Guarnizo, I. Miliou, and P. Papapetrou, “Impact of Dimensionality on Nowcasting Seasonal Influenza with Environmental Factors“. In the International Symposium on Intelligent Data Analysis (IDA), 128-142, 2022
  5. J. Parsa, C. R. Rojas, and H. Hjalmarsson. “Optimal input design for sparse system identification”. In Proceedings of the 2022 European Control Conference (ECC), 2022
  6. B. Lakshminarayanan and C. R. Rojas. “A statistical decision-theoretical perspective on the two-stage approach to parameter estimation”. In Proceedings of the 61th IEEE Conference on Decision and Control (CDC 2022) (accepted for publication), 2022
  7. I. Lourenço, R. Winqvist, C. R. Rojas, and B. Wahlberg. “A teacher-student Markov decision process-based framework for online correctional learning”. In Proceedings of the 61th IEEE Conference on Decision and Control (CDC 2022) (accepted for publication), 2022
  8. Z. Wang, I. Samsten, and P. Papapetrou, Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients. In Artificial Intelligence in Medicine (AIME), 338-348, 2021 [best student paper award]
  9. J.PavlopoulosandP.Papapetrou,CustomizedNeuralPredictiveMedicalText:AUse- Case on Caregivers. In Artificial Intelligence in Medicine (AIME), 438-443, 2021
  10. J. Rebane, I. Samsten, P. Pantelidis, and P. Papapetrou, Assessing the Clinical Validity of Attention-based and SHAP Temporal Explanations for Adverse Drug Event Predictions. In Computer-based Medical Systems (CBMS), 235-240, 2021
  11. Z. Wang, I. Samsten, R. Mochaourab, and P. Papapetrou, Learning Time Series Counterfactuals via Latent Space Representations. In Discovery Science (DS), 369- 384, 2021
  12. I. Lourenço, R. Mattila, C. R. Rojas, and B. Wahlberg. “Cooperative system identification via correctional learning”. In Proceedings of the 19th IFAC Symposium on System Identification (SYSID 2021), 2021
  13. R. A. González, C. R. Rojas, and H. Hjalmarsson. “Non-causal regularized least- squares for continuous-time system identification with band-limited input excitations”. In Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021), 2021
  14. R. A. González, C. R. Rojas, S. Pan, and J. S. Welsh. “The SRIVC algorithm for continuous-time system identification with arbitrary input excitation in open and closed loop”. In Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021), 2021
  15. Z. Lee, T. Lindgren, and P. Papapetrou, “Z-Miner: an efficient method for mining frequent arrangements of event intervals“. In ACM Knowledge Discovery and Data Mining (KDD), 524-534, 2020
  16. Z. Lee, S. Girdzijauskas, and P. Papapetrou, “Z-Embedding: A spectral representation of event intervals for efficient clustering and classification“. In the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML/PKDD), 710-726, 2020
  17. M. Bampa, P. Papapetrou, and J. Hollmen, “A clustering framework for patient phenotyping with application to adverse drug events“. In IEEE Computer-Based Medical Systems (CBMS), 177-182, 2020
  18. J. Pavlopoulos and P. Papapetrou, “Clinical predictive keyboard using statistical and neural language modeling“. In IEEE Computer-Based Medical Systems (CBMS), 293- 296, 2020
  19. Z. Lee, J. Rebane, and P. Papapetrou, “Mining disproportional frequent arrangements of event intervals for investigating adverse drug events“. In IEEE Computer-Based Medical Systems (CBMS), 293-296, 2020
  20. N. B. Kumarakulasinghe, T. Blomberg, J. Liu, A. S. Leao and P. Papapetrou, “Evaluating local interpretable model-agnostic explanations on clinical machine learning classification models“. In IEEE Computer-Based Medical Systems (CBMS), 7-12, 2020
  21. R. Mattila, I. Lourenço, V. Krishnamurthy, C. R. Rojas, and B. Wahlberg. “What did your adversary believe? Optimal smoothing in counter-autonomous systems”. In Proceedings of the 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Barcelona, Spain, 2020
  22. R. A. González and C. R. Rojas. “Finite sample deviation and variance bounds for first order autoregressive processes”. In Proceedings of the 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Barcelona, Spain, 2020
  23. R. A. González and C. R. Rojas. “A finite-sample deviation bound for stable autoregressive processes”. In Proceedings of the 2nd Conference on Learning for Decision and Control (L4DC), Berkeley, USA, 2020
  24. R. A. González, J. S. Welsh, and C. R. Rojas. “Enforcing stability through ellipsoidal inner approximations in the indirect approach for continuous-time system identification”. In Proceedings of the 21st IFAC World Congress (IFAC 2020), Berlin, Germany, 2020
  25. R. Mattila, C. R. Rojas, E. Moulines, V. Krishnamurthy, and B.Wahlberg. “Fast and consistent learning of hidden Markov models by incorporating non-consecutive correlations”. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), 2020
  26. I. Lourenço, R. Mattila, C. R. Rojas, and B. Wahlberg. “How to protect your privacy? A framework for counter-adversarial decision making”. In Proceedings of the 59th IEEE Conference on Decision and Control (CDC 2020), 2020
  27. R. Mochaourab, S. Sinha, S. Greenstein, and P. Papapetrou, “Robust Counterfactual Explanations for Privacy-Preserving SVMs,” International Conference on Machine Learning (ICML 2021), Workshop on Socially Responsible Machine Learning, Jul. 2021

Demo paper (D)

  1. R. Mochaourab, S. Sinha, S. Greenstein, and P. Papapetrou, “Demonstrator on Counterfactual Explanations for Differentially Private Support Vector Machines,” European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022), Demo Track, Sept. 2022