{"id":11392,"date":"2023-01-27T17:49:51","date_gmt":"2023-01-27T16:49:51","guid":{"rendered":"https:\/\/www.digitalfutures.kth.se\/?page_id=11392"},"modified":"2023-01-27T17:49:51","modified_gmt":"2023-01-27T16:49:51","slug":"publications","status":"publish","type":"page","link":"https:\/\/wpmu-tris.sys.kth.se\/digitalfutures\/research\/collaborative-projects\/on-going-collaborative-projects\/extremum\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

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

Journal articles (J)<\/em><\/p>\n

    \n
  1. R.A.Gonza\u0301lez,C.R.Rojas,S.Pan,andJ.Welsh.\u201cTheoreticalandpracticalaspects of the convergence of the SRIVC estimator for over-parameterized models\u201d. Automatica, 142:110355, 2022<\/li>\n
  2. S. Pan, J. Welsh, R. A. Gonza\u0301lez, and C. R. Rojas. \u201cConsistency analysis and bias elimination of the instrumental variable based state variable filter method\u201d. Automatica, 144:110511, 2022<\/li>\n
  3. P. Wachel and C. R. Rojas. \u201cAn adversarial approach to adaptive model predictive control\u201d. Journal of Advances in Applied & Computational Mathematics, 9, 135\u2013146, 2022<\/li>\n
  4. R. A. Gonza\u0301lez, C. R. Rojas, S. Pan, and J. Welsh. \u201cRefined instrumental variable methods for unstable continuous-time systems in closed-loop\u201d. International Journal of Control (accepted for publication), 2022<\/li>\n
  5. C.R.RojasandP.Wachel.\u201cOnstate-spacerepresentationsofgeneraldiscrete-time dynamical systems\u201d. IEEE Transactions on Automatic Control (accepted for publication), 2022<\/li>\n
  6. R.Mochaourab,A.Venkitaraman,I.Samsten,P.Papapetrou,andC.R.Rojas,\u201cPost- hoc Explainability for Time Series Classification: Toward a Signal Processing Perspective,\u201d IEEE Signal Processing Magazine, Special Issue on Explainability in Data Science: Interpretability, Reproducibility, and Replicability, vol. 39, no. 4, Jul. 2022<\/li>\n
  7. S. Greenstein, P. Papapetrou, and R. Mochaourab, \u201cEmbedding Human Values into Artificial Intelligence,\u201d in De Vries, Katja (ed.), De Lege, Iustus fo\u0308rlag, Uppsala University, 2022<\/li>\n
  8. J. Rebane, I. Samsten, L. Bornemann, and P. Papapetrou, \u201cSMILE: A feature-based temporal abstraction framework for event-interval sequence classification\u201d. In Data Mining and Knowledge Discovery (DAMI) 35(1): 372-399, 2021<\/li>\n
  9. B. Djehiche, O. Mazhar, and C. R. Rojas. \u201cFinite impulse response models: A nonasymptotic analysis of the least squares estimator\u201d. Bernoulli, 27(2):976\u20131000, 2021<\/li>\n
  10. R. A. Gonza\u0301lez, C. R. Rojas, S. Pan, and J. Welsh. \u201cConsistent identification of continuous-time systems under multisine input signal excitation\u201d. Automatica, 133:109859, 2021<\/li>\n
  11. I. Lourenc\u0327o, R. Mattila, C. R. Rojas, X. Hu, and B.Wahlberg. \u201cHidden Markov models: Inverse filtering, belief estimation and privacy protection\u201d. Journal of Systems Science and Complexity, 34:1801\u20131820, 2021<\/li>\n
  12. J. Rebane, I. Samsten, and P. Papapetrou, \u201cExploiting Complex Medical Data with Interpretable Deep Learning for Adverse Drug Event Prediction\u201d. In Artificial Intelligence in Medicine (AIM), 28(8): 1651-1659, 2021<\/li>\n
  13. Locally and globally explainable time series tweaking KAIS), 62 (5): 1671- 1700, 2020<\/li>\n
  14. R. Mattila, C. R. Rojas, V. Krishnamurthy, and B. Wahlberg. \u201cInverse filtering for hidden Markov models with applications to counter-adversarial autonomous systems\u201d. IEEE Transactions on Signal Processing, 68:4987\u20135002, 2020<\/li>\n<\/ol>\n

    Conference papers (C)<\/em><\/p>\n

      \n
    1. Z. Lee, M. Trincavelli, and P. Papapetrou, \u201cFinding Local Groupings of Time Series\u201c, In the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML\/PKDD), to appear<\/li>\n
    2. L. Mondrejevski, I. Miliou, A. Montanino, D. Pitts, J. Hollme\u0301n, and P. Papapetrou, \u201cFLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction\u201d. In Computer-Based Medical Systems (CBMS), to appear<\/li>\n
    3. M. Bampa, T. Fasth, S. Magnusson, and P. Papapetrou, \u201cEpidRLearn: Learning intervention strategies for epidemics with Reinforcement Learning\u201c. In the International Conference on Artificial Intelligence in Medicine (AIME), 2022<\/li>\n
    4. S. Guarnizo, I. Miliou, and P. Papapetrou, \u201cImpact of Dimensionality on Nowcasting Seasonal Influenza with Environmental Factors\u201c. In the International Symposium on Intelligent Data Analysis (IDA), 128-142, 2022<\/li>\n
    5. J. Parsa, C. R. Rojas, and H. Hjalmarsson. \u201cOptimal input design for sparse system identification\u201d. In Proceedings of the 2022 European Control Conference (ECC), 2022<\/li>\n
    6. B. Lakshminarayanan and C. R. Rojas. \u201cA statistical decision-theoretical perspective on the two-stage approach to parameter estimation\u201d. In Proceedings of the 61th IEEE Conference on Decision and Control (CDC 2022) (accepted for publication), 2022<\/li>\n
    7. I. Lourenc\u0327o, R. Winqvist, C. R. Rojas, and B. Wahlberg. \u201cA teacher-student Markov decision process-based framework for online correctional learning\u201d. In Proceedings of the 61th IEEE Conference on Decision and Control (CDC 2022) (accepted for publication), 2022<\/li>\n
    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]<\/li>\n
    9. J.PavlopoulosandP.Papapetrou,CustomizedNeuralPredictiveMedicalText:AUse- Case on Caregivers. In Artificial Intelligence in Medicine (AIME), 438-443, 2021<\/li>\n
    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<\/li>\n
    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<\/li>\n
    12. I. Lourenc\u0327o, R. Mattila, C. R. Rojas, and B. Wahlberg. \u201cCooperative system identification via correctional learning\u201d. In Proceedings of the 19th IFAC Symposium on System Identification (SYSID 2021), 2021<\/li>\n
    13. R. A. Gonza\u0301lez, C. R. Rojas, and H. Hjalmarsson. \u201cNon-causal regularized least- squares for continuous-time system identification with band-limited input excitations\u201d. In Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021), 2021<\/li>\n
    14. R. A. Gonza\u0301lez, C. R. Rojas, S. Pan, and J. S. Welsh. \u201cThe SRIVC algorithm for continuous-time system identification with arbitrary input excitation in open and closed loop\u201d. In Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021), 2021<\/li>\n
    15. Z. Lee, T. Lindgren, and P. Papapetrou, \u201cZ-Miner: an efficient method for mining frequent arrangements of event intervals\u201c. In ACM Knowledge Discovery and Data Mining (KDD), 524-534, 2020<\/li>\n
    16. Z. Lee, S. Girdzijauskas, and P. Papapetrou, \u201cZ-Embedding: A spectral representation of event intervals for efficient clustering and classification\u201c. In the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML\/PKDD), 710-726, 2020<\/li>\n
    17. M. Bampa, P. Papapetrou, and J. Hollmen, \u201cA clustering framework for patient phenotyping with application to adverse drug events\u201c. In IEEE Computer-Based Medical Systems (CBMS), 177-182, 2020<\/li>\n
    18. J. Pavlopoulos and P. Papapetrou, \u201cClinical predictive keyboard using statistical and neural language modeling\u201c. In IEEE Computer-Based Medical Systems (CBMS), 293- 296, 2020<\/li>\n
    19. Z. Lee, J. Rebane, and P. Papapetrou, \u201cMining disproportional frequent arrangements of event intervals for investigating adverse drug events\u201c. In IEEE Computer-Based Medical Systems (CBMS), 293-296, 2020<\/li>\n
    20. N. B. Kumarakulasinghe, T. Blomberg, J. Liu, A. S. Leao and P. Papapetrou, \u201cEvaluating local interpretable model-agnostic explanations on clinical machine learning classification models\u201c. In IEEE Computer-Based Medical Systems (CBMS), 7-12, 2020<\/li>\n
    21. R. Mattila, I. Lourenc\u0327o, V. Krishnamurthy, C. R. Rojas, and B. Wahlberg. \u201cWhat did your adversary believe? Optimal smoothing in counter-autonomous systems\u201d. In Proceedings of the 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Barcelona, Spain, 2020<\/li>\n
    22. R. A. Gonza\u0301lez and C. R. Rojas. \u201cFinite sample deviation and variance bounds for first order autoregressive processes\u201d. In Proceedings of the 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Barcelona, Spain, 2020<\/li>\n
    23. R. A. Gonza\u0301lez and C. R. Rojas. \u201cA finite-sample deviation bound for stable autoregressive processes\u201d. In Proceedings of the 2nd Conference on Learning for Decision and Control (L4DC), Berkeley, USA, 2020<\/li>\n
    24. R. A. Gonza\u0301lez, J. S. Welsh, and C. R. Rojas. \u201cEnforcing stability through ellipsoidal inner approximations in the indirect approach for continuous-time system identification\u201d. In Proceedings of the 21st IFAC World Congress (IFAC 2020), Berlin, Germany, 2020<\/li>\n
    25. R. Mattila, C. R. Rojas, E. Moulines, V. Krishnamurthy, and B.Wahlberg. \u201cFast and consistent learning of hidden Markov models by incorporating non-consecutive correlations\u201d. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), 2020<\/li>\n
    26. I. Lourenc\u0327o, R. Mattila, C. R. Rojas, and B. Wahlberg. \u201cHow to protect your privacy? A framework for counter-adversarial decision making\u201d. In Proceedings of the 59th IEEE Conference on Decision and Control (CDC 2020), 2020<\/li>\n
    27. R. Mochaourab, S. Sinha, S. Greenstein, and P. Papapetrou, \u201cRobust Counterfactual Explanations for Privacy-Preserving SVMs,\u201d International Conference on Machine Learning (ICML 2021), Workshop on Socially Responsible Machine Learning, Jul. 2021<\/li>\n<\/ol>\n

      Demo paper (D)<\/em><\/p>\n

        \n
      1. R. Mochaourab, S. Sinha, S. Greenstein, and P. Papapetrou, \u201cDemonstrator on Counterfactual Explanations for Differentially Private Support Vector Machines,\u201d European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022), Demo Track, Sept. 2022<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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