International Journals
  1. Pillonetto, G., G. Sparacino, and C. Cobelli. Reconstructing insulin secretion rate after a glucose stimulus by an improved stochastic deconvolution method. IEEE Transactions on Biomedical Engineering 48:1352-1354, 2001.
  2. Sparacino, G., G. Pillonetto, M. Capello, G. De Nicolao, C. Cobelli WINSTODEC: a stochastic deconvolution interactive program for physiological and pharmacokinetic systems, Computer Methods and Programs in Biomedicine 67: 67-77, 2002.
  3. Pillonetto, G., G. Sparacino, P. Magni, R. Bellazzi, C. Cobelli. Minimal model S(I)=0 problem in NIDDM subjects: nonzero Bayesian estimates with credible confidence intervals. American Journal of Physiology: Endocrinology and Metabolism, 282(3): E564-573, 2002.
  4. Pillonetto, G., G. Sparacino, C. Cobelli. Handling non-negativity in deconvolution of physiological signals: a nonlinear stochastic approach, Annals of Biomedical Engineering, 8: 1077-1087, 2002.
  5. Pillonetto, G., G. Sparacino, C. Cobelli. Numerical non-identifiability regions of the minimal model of glucose kinetics: superiority of Bayesian estimation, Mathematical Biosciences, 184(1):53-67, 2003.
  6. Pillonetto, G., B.M. Bell. Deconvolution of nonstationary physical signals: a smooth variance model for insulin secretion rate, Inverse Problems, 20(2): 367-383, 2004.
  7. Bell, B.M., G. Pillonetto Estimating parameters and stochastic functions of one variable using nonlinear measurements models, Inverse Problems, 20(3): 627-646, 2004.
  8. Carpin, S., G. Pillonetto Motion planning using adaptive random walks, IEEE Transactions on Robotics and Automation, 21(1). Pages 129-136, 2005
  9. Pillonetto, G., A. Caumo, G. Sparacino, C. Cobelli. A new dynamic index of insulin sensitivity, IEEE Transactions on Biomedical Engineering, 53(3): 369-79, 2006.
  10. Pillonetto, G., M.P. Saccomani. Input estimation in nonlinear dynamic systems using differential algebra techniques, Automatica, 42(12): 1117-1129, 2006
  11. Pillonetto, G., C. Cobelli. Identifiability of the stochastic semi-blind deconvolution problem using a class of time-invariant linear systems, Automatica, 43(4): 647-654, 2007
  12. Pillonetto, G., B. Bell. Bayes and empirical Bayes semi-blind deconvolution using eigenfunctions of a prior covariance, Automatica 43(10): 1698-1712, 2007
  13. Pillonetto, G., B. Bell. Optimal smoothing of non-linear dynamic systems via Monte Carlo Markov chains, Automatica 44(7): 1676-1785, 2008
  14. Pillonetto, G. Solutions of nonlinear control and estimation problems in Reproducing Kernel Hilbert Spaces: existence and numerical determination, Automatica 44(8): 2135-2141, 2008
  15. Cinquemani, E., G. Pillonetto. Wavelet estimation by Bayesian thresholding and model selection, Automatica 44(9): 2288-2297, 2008
  16. Pillonetto, G. Identification of time-varying systems in Reproducing Kernel Hilbert Spaces, IEEE Transactions on Automatic Control 53(9): 2202 - 2209, 2008
  17. Pillonetto, G., G. De Nicolao, M. Chierici, C. Cobelli. Fast algorithms for nonparametric population modeling of large data sets, Automatica 45: 173--179, 2009
  18. Bell, B., J. Burke, G. Pillonetto. An inequality constrained nonlinear Kalman-Bucy smoother by interior point likelihood maximization, Automatica 45: 25-33, 2009
  19. Zanderigo, F., A. Bertoldo, G. Pillonetto, C. Cobelli. Nonlinear Stochastic Regularization to Characterize Tissue Residue Function in Bolus-Tracking MRI: Assessment and Comparison With SVD, Block-Circulant SVD, and Tikhonov, IEEE Transactions on Biomedical Engineering, 56(5): 1287-1297, 2009.
  20. Pillonetto, G., A. Chiuso. Fast computation of smoothing splines subject to equality constraints, Automatica 45: 2842-2849, 2009
  21. Pillonetto, G., F. Dinuzzo, G. De Nicolao. Bayesian on-line multi-task learning of Gaussian processes, IEEE Trans. on Pattern Analysis and Machine Intelligence vol, 32(2), pp. 193-205, 2010
  22. Pillonetto, G., A. Caumo, C. Cobelli. Dynamic index of insulin sensitivity: importance in diabetes, American Journal of Physiology: Endocrinology and Metabolism 298(3), E440-E448, 2010
  23. Pillonetto, G., G. De Nicolao. A new kernel-based approach for linear system identification, Automatica vol. 46(1), pp. 81-93, 2010
  24. Pillonetto, G., A. Chiuso and G. De Nicolao. Prediction error identification of linear systems: a nonparametric Gaussian regression approach, Automatica vol. 47(2), pp. 291-305, 2011
  25. Dinuzzo, F, G. Pillonetto and G. De Nicolao. Client-server multi-task learning from distributed datasets, IEEE Transactions on Neural Networks, vol. 22(2):290-303, 2011.
  26. Peruzzo, D., F. Zanderigo, A. Bertoldo, G. Pillonetto, M. Cosottini and C. Cobelli. Assessment on clinical data of nonlinear stochastic deconvolution versus Singular Value Decomposition for quantitative Dynamic Susceptibility Contrast-Magnetic Resonance Imaging, Magnetic Resonance Imaging vol. 29(7), pp. 927-936, 2011
  27. Pillonetto, G., M.H. Quang and A. Chiuso. A new kernel-based approach for nonlinear system identification, IEEE Trans. on Automatic Control vol. 56(12), pp. 2825-2840, 2011
  28. Aravkin, A., B.M. Bell, J.V. Burke and G. Pillonetto. An l1-Laplace robust Kalman smoother, IEEE Trans. on Automatic Control vol. 56(12), pp. 2898-2911, 2011
  29. Chiuso, A. and G. Pillonetto. A Bayesian approach to sparse dynamic network identification, Automatica vol. 48 (8), pp. 1553-1565, 2012
  30. Pillonetto, G., G. Erinc and S. Carpin. Online estimation of covariance parameters using extended Kalman filtering and application to robot localization. Advanced Robotics, 26(18):2169-2188, 2012
  31. Quer, G., R. Masiero, G. Pillonetto, M. Rossi and M. Zorzi. Sensing, Compression and Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework, IEEE Transactions on Wireless Communications vol. 11 (10), pp. 3447 - 3461, 2012
  32. Varagnolo, D., G. Pillonetto and L. Schenato. Distributed parametric and nonparametric regression with on-line performance bounds computation. Automatica vol. 48 (10), pp. 2468-2481, 2012
  33. D'Avanzo, C. A. Goljahani, G. Pillonetto, G. De Nicolao, G. Sparacino. A multi-task learning approach for the extraction of single-trial evoked potentials. Computer methods and programs in biomedicine, 110(2): 125-136, 2013
  34. Pillonetto, G., B. Bell and S. Del Favero. Distributed Kalman smoothing in static Bayesian networks. Automatica 49(4): 1001-1011, 2013
  35. Varagnolo, D., L. Schenato and G. Pillonetto. A variation of the Newton-Pepys problem and its connections to size-estimation problems, Statistics & Probability Letters 83(5): 1472-1478, 2013
  36. Pillonetto, G. Consistent identification of Wiener systems: a machine learning viewpoint, Automatica 49(9): 2704-2712, 2013
  37. Varagnolo, D., S. Del Favero, F. Dinuzzo, L. Schenato and G. Pillonetto. Finding Potential Support Vectors in separable classification problems, IEEE Transactions on Neural Networks and Learning Systems 24(11): 1799-1813, 2013
  38. Bottegal, G. and G. Pillonetto. Regularized spectrum estimation using stable spline kernels. Automatica 49(11): 3199-3209, 2013
  39. Aravkin, A., J.V. Burke and G. Pillonetto. Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory. Journal of Machine Learning Research 14: 2689-2728, 2013
  40. D. Varagnolo, G. Pillonetto and L. Schenato. Distributed cardinality estimation in anonymous networks. IEEE Transactions on Automatic Control 59: 645-659, 2014
  41. Aravkin, A., J.V. Burke, A. Chiuso and G. Pillonetto. Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ARD and GLasso. Journal of Machine Learning Research 15: 217-252, 2014
  42. Pillonetto, G., F. Dinuzzo, T. Chen, G. De Nicolao and L. Ljung. Kernel Methods in System Identification, Machine Learning and Function Estimation: A Survey, Automatica 50(3): 657-682, 2014.
  43. Chen, T., M. Andersen, L. Ljung, A. Chiuso and G. Pillonetto. System Identification via Sparse Multiple Kernel-Based Regularization Using Sequential Convex Optimization Techniques, IEEE Trans. on Automatic Control, 59(11): 2933 - 2945, 2014
  44. Aravkin, A., J.V. Burke and G. Pillonetto. Robust and Trend Following Student's t Kalman Smoothers, SIAM Journal on Control and Optimization, 52(5): 2891-2916, 2014
  45. Castellaro, C., D. Peruzzo, A. Mehndiratta, G. Pillonetto, E.T. Petersen, X. Golay, M.A. Chappell and A. Bertoldo. Estimation of arterial arrival time and cerebral blood flow from QUASAR arterial spin labeling using stable spline Magnetic Resonance in Medicine 74 (6), pp. 1758-1767, 2015
  46. Aravkin, A., B. Bell, J.V. Burke and G. Pillonetto. The connection between Bayesian estimation of a Gaussian random field and RKHS, IEEE Transactions on Neural Networks and Learning Systems 26(7): 1518 - 1524, 2015
  47. Pillonetto, G., and A. Chiuso. Tuning complexity in regularized kernel-based regression and linear system identification: the robustness of the marginal likelihood estimator, 58: 106-117, Automatica 2015
  48. Dalla Man, C., G. Pillonetto, M. Riz and C. Cobelli. An index of parameter reproducibility accounting for estimation uncertainty: theory and case study on beta-cell responsivity and insulin sensitivity, American Journal of Physiology - Endocrinology and Metabolism 308 (11), pp. E971-E977, 2015
  49. Varagnolo, D., F. Zanella, A. Cenedese, G. Pillonetto and L. Schenato. Newton-Raphson Consensus for Distributed Convex Optimization, IEEE Transactions on Automatic Control 61(4) 2016
  50. Chen, T., T. Ardeshiri, F.P. Carli, A. Chiuso, L. Ljung and G. Pillonetto. Maximum entropy properties of discrete-time first-order stable spline kernel, Automatica 66 pp. 34-38 2016
  51. Bottegal, G., A. Aravkin, H. Hjalmarsson and G. Pillonetto. Robust EM kernel-based methods for linear system identification, Automatica 67 pp. 114-126 2016
  52. Pillonetto, G., T. Chen, A. Chiuso, G. De Nicolao and L. Ljung. Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint, Automatica 69 pp. 137-149 2016
  53. Pillonetto, G. A new kernel-based approach to hybrid system identification, Automatica 70 pp. 21-31 2016
  54. Peruzzo, D., M. Castellaro G. Pillonetto and A. Bertoldo. Stable spline deconvolution for dynamic susceptibility contrast MRI, Magn Reson Med, 2017.
  55. Prando, G., A. Chiuso and G. Pillonetto. Maximum Entropy Vector Kernels for MIMO system identification, Automatica 79 pp. 326-339 2017
  56. Todescato, M., A. Carron, R. Carli, G. Pillonetto and L. Schenato. Multi-Robots Gaussian Estimation and Coverage Control: from Client-Server to Peer-to-Peer Architectures, Automatica 80 pp. 284-294 2017
  57. Bottegal. G., H. Hjalmarsson and G. Pillonetto. A new kernel-based approach to system identification with quantized output data, Automatica 85 pp. 145-152 2017
  58. Aravkin, A., J. Burke, L. Ljung, A. Lozano and G. Pillonetto. Generalized Kalman Smoothing: Modeling and Algorithms, Automatica 86 pp. 63-86 2017
  59. Darwish, M., G. Pillonetto and R. Toth. The Quest for the Right Kernel in Bayesian Impulse Response Identification: The Use of OBFs, Automatica 87 pp. 318-329 2018
  60. Bottegal, G. and G. Pillonetto. The Generalized Cross Validation filter, Automatica 90 pp. 130-137 2018
  61. G. Pillonetto. System identification using kernel-based regularization: new insights on stability and consistency issues, Automatica 93, pp. 321-332 2018
  62. Chen, T. and G. Pillonetto. On the stability of reproducing kernel Hilbert spaces of discrete-time impulse responses, Automatica 95, pp. 529-533 2018
  63. Aravkin, A., J. Burke and G. Pillonetto. Generalized system identification with stable spline kernels, SIAM Journal on Scientific Computing Vol. 40, No. 5, pp. 1419-1443 2018
  64. Darwish, M.A.H., P. Cox, I. Proimadis, G. Pillonetto and R. Toth. Prediction-Error Identification of LPV Systems: A Nonparametric Gaussian Regression Approach. Automatica 97, pp. 92-103 2018
  65. Chiuso, A. and G. Pillonetto. System Identification: A Machine Learning Perspective, Annual Review of Control, Robotics, and Autonomous Systems 2(1) 2019
  66. Pillonetto, G., A. Chiuso and G. De Nicolao. Stable spline identification of linear systems under missing data, Vol. 108, Automatica 2019
  67. Aravkin, A., G. Bottegal and G. Pillonetto. Boosting as a kernel-based method, 108 (11) pp.1951-1974, Machine Learning 2019
  68. Jonker, J., A. Aravkin, J.V. Burke, G. Pillonetto and S. Webster. Fast Robust Methods for Singular State-Space Models, Automatica 105 pp. 399-405, 2019
  69. Pillonetto, G., L. Schenato and D. Varagnolo. Distributed multi-agent Gaussian regression via finite-dimensional approximations, IEEE Trans. on Pattern Analysis and Machine Intelligence, 41 pp. 2098-2111, 2019
  70. Todescato, M., A. Carron, R. Carli, G. Pillonetto and L. Schenato. Efficient spatio-temporal Gaussian regression via Kalman filtering, Automatica Vol. 118, 2020
  71. Bisiacco, M. and G. Pillonetto. Kernel absolute summability is sufficient but not necessary for RKHS stability, SIAM Journal on Control and Optimization 58(4), pp. 2006-2022, 2020
  72. Bisiacco, M. and G. Pillonetto. On the mathematical foundations of stable RKHSs, Automatica Vol. 118, 2020
  73. Scampicchio, A., A. Aravkin and G. Pillonetto. Stable and robust LQR design via scenario approach, Automatica, 2021
  74. Dalla Libera, A., R. Carli and G. Pillonetto Kernel-based methods for Volterra series identification, Automatica, 2021
  75. Pillonetto, G., M. Bisiacco, G. Palu' and C. Cobelli. Tracking the time course of reproduction number and lockdown's effect on human behaviour during SARS-CoV-2 epidemic: nonparametric estimation, Scientific reports, 2021
  76. Bisiacco, M. and G. Pillonetto. COVID-19 epidemic control using short-term lockdowns for collective gain, Annual Reviews in Control, 2021
  77. Dalla Libera, A. and G. Pillonetto. Deep prediction networks, Neurocomputing Vol. 469, p. 321-329, 2022
  78. Faccioli, S., A. Facchinetti, G. Sparacino, G. Pillonetto and S. Del Favero. Linear Model Identification for Personalized Prediction and Control in Diabetes, IEEE Transactions on Biomedical Engineering, 69 (2), 2022
  79. Dal Fabbro, N., M. Rossi, G. Pillonetto, L. Schenato and G. Piro. Model-free radio map estimation in massive MIMO systems via semi-parametric Gaussian regression, IEEE Wireless Communications Letters Vol. 11 (3), 2022
  80. Pillonetto, G. and A. Chiuso. Linear system identification using the sequential stabilizing spline algorithm, Automatica, Vol. 138 (110169), 2022
  81. Bisiacco, M., G. Pillonetto and C. Cobelli. Closed-form expressions and nonparametric estimation of COVID-19 infection rate, Automatica, 140 (110265) 2022
  82. Pillonetto, G. and A. Scampicchio. Sample complexity and minimax properties of exponentially stable regularized estimators, IEEE Transactions on Automatic Control, 67 (5) 2022
  83. Pillonetto, G. and A. Yazdani. Sparse estimation in linear dynamic networks using the stable spline horseshoe prior, Automatica, 146 (110666), 2022
  84. Baggio, G., A. Care', A. Scampicchio and G. Pillonetto. Bayesian frequentist bounds for machine learning and system identification, Automatica, 146 (110599), 2022
  85. Care', A., R. Carli, A. Dalla Libera, D. Romeres and G. Pillonetto. Kernel methods and Gaussian processes for system identification and control, IEEE Control Systems Magazine, 2023
  86. Scampicchio, M. Bisiacco and G. Pillonetto. Kernel-based learning of orthogonal functions Neurocomputing, 2023
  87. Pillonetto, G. and L. Ljung. Full Bayesian identification of linear dynamic systems using stable kernels, Proceedings of the National Academy of Sciences USA, 120(18), 2023
  88. Dalla Libera, A., C. Toffanin, M. Drecogna, A. Galderisi, G. Pillonetto and C. Cobelliu. In silico design and validation of a time-varying PID controller for an artificial pancreas with intraperitoneal insulin delivery and glucose sensing, APL Bioengineering, 7(2), 2023
  89. Bisiacco, M. and G. Pillonetto. Sliding-mode theory under feedback constraints and the problem of epidemic control, SIAM journal on applied mathematics, 2023
  90. Pillonetto, G. and M. Bisiacco. Kernel-based linear system identification: when does the representer theorem hold? Automatica 159, 111347, 2024
  91. Bisiacco, M. and G. Pillonetto. Learning for Control: L1-error Bounds for Kernel-based Regression, IEEE Transactions on Automatic Control, 2024
  92. Bisiacco, M. and G. Pillonetto. A refinement of the stability test for reproducing kernel Hilbert spaces, Automatica 163, 111606, 2024
  93. Cao, W. and G. Pillonetto. Dealing with collinearity in large-scale linear system identification using Gaussian regression, Automatica 166, 111708, 2024
  94. Bisiacco, M. and G. Pillonetto. Mercer kernel absolute integrability is only sufficient for RKHS stability, Systems & Control Letters 191, 105885, 2024
Papers in Proceedings of International Conferences
  1. Pillonetto G. Stochastic deconvolution of nonnegative physical signals. In "Proceedings of the Conference on Applied Inverse Problems: Theoretical and Computational Aspects. Montecatini, June 18-22, 2001".
  2. Pillonetto, G. Bayesian deconvolution of functions in Reproducing Kernel Hilbert Spaces using MCMC techniques. In "Book of abstracts of the 21st IFIP TC 7 Conference on System Modeling and Optimization, Sophia Antilopis, France, July 21-25, 2003".
  3. Carpin, S., G. Pillonetto. Robot motion planning using adaptive random walks. In "Proceedings of IEEE Int. Conf. on Robotics and Automation, Taipei, Taiwan, May 12-17, 2003".
  4. Corazza S., G. Pillonetto, C. Cobelli, R. Frezza. Numerical approach to skin artifacts correction in stereophotogrammetry. In "Proceedings of the conference of the American Society of Biomechanics, Toledo, OH, USA, September 25-29, 2003"
  5. Carpin S., G. Pillonetto. Learning sampling distributions for randomized motion planning: role of history size. In "Proceedings of the 2003 conference on Artificial Intelligence and Applications, Balmadena, Spain, September 8-10, 2003"
  6. Carpin, S., G. Pillonetto. Centralized multi-robot motion planning: a random walks based approach. Intelligent Autonomous Systems 8 IOS Press. 2004, pag. 610-617
  7. Zanderigo, F., A. Bertoldo, G. Pillonetto, M. Cosottini, C. Cobelli Nonlinear stochastic regularization to characterize tissue residue function from bolus-tracking MRI images. In Proceedings of the Workshop on MRI: a technical perspective, 2004, Venice, Italy, pag. 77-78
  8. Bertoldo, A., S. Corazza, F. Zanderigo, G. Pillonetto, M. Cosottini, C. Cobelli Assessment of regional cerebral blood flow by bolus-tracking MRI images: characterization of the tissue residue function using nonlinear stochastic regularization method. In CD-ROM proceedings of the conference Medicon 2004, 2004, Ischia, Italy
  9. Carpin, S., G. Pillonetto. Merging the adaptive random walks planner with the randomized potential field planner. In proceedings of the 2005 IEEE International workshop on Robot Motion and Control, pag. 151-156
  10. Pillonetto, G., M.P. Saccomani. Estimating inputs of nonlinear dynamical systems using differential algebra techniques. In proceedings of the 16-th IFAC World Congress, 2005 Praha.
  11. Pillonetto, G., A. Caumo, C. Cobelli. Insulin sensitivity index also accounting for insulin action dynamics: importance in diabetes. In proceedings of the 6-th IFAC Symposium on modeling and control in biomedical systems, 2006, Reims, pag. 217-212.
  12. Chierici, M., G. Pillonetto, G. Toffolo, C. Cobelli. Glucose Production by Deconvolution in Intravenous and Oral Glucose Tolerance Tests: Role of Output Variable. In proceedings of 28th Annual International Conference of the IEEE, EMBS '06, 2006, New York, pag. 5045-5048.
  13. Pillonetto, G., B. Bell. Bayes and Empirical Bayes Semi-Blind Deconvolution. In proceedings of AIP 2007, Conference on Applied Inverse Problems 2007: Theoretical and Computational Aspects June 25-29, 2007, Vancouver, Canada.
  14. Bell, B., G. Pillonetto. MCMC Estimation of Nonlinear Dynamical Systems. In proceedings of AIP 2007, Conference on Applied Inverse Problems 2007: Theoretical and Computational Aspects June 25-29, 2007, Vancouver, Canada.
  15. De Nicolao, G., G. Pillonetto, M. Chierici, C. Cobelli Efficient Nonparametric Population Modeling for Large Data Sets, Proceedings of the American Control Conference, July 9-13, 2007, New York, pag. 2921 - 2926
  16. Pillonetto, G., S. Carpin. Multirobot localization with unknown variance parameters using iterated Kalman filtering. In Proceedings of the 2007 IEEE International Conference on Intelligent Robots and Systems, Oct 29 - 2 Nov, 2007, San Diego, CA, USA
  17. Pillonetto, G., C. Cobelli. Predictive power of indices derived from models of biological dynamic systems, Proceedings of the American Control Conference, June 11-13, 2008, Seattle, USA
  18. Pillonetto, G., F. Dinuzzo, G. De Nicolao. Bayesian online multi-task learning using regularization networks, Proceedings of the American Control Conference, June 11-13, 2008, Seattle, USA
  19. De Nicolao, G., G. Pillonetto. A new kernel-based approach for system identification, Proceedings of the American Control Conference, June 11-13, 2008, Seattle, USA
  20. Pillonetto, G., A. Chiuso, G. De Nicolao. Identification and prediction of linear dynamical systems: a learning theory approach, Proceedings of the International Conference on Mathematical Problems in Engineering, Aerospace and Sciences, June 25-27, 2008, Genova, Italy
  21. Dinuzzo, F., G. Pillonetto, G. De Nicolao. A recursive Bayesian approach to multitask learning, Proceedings of the International Conference on Mathematical Problems in Engineering, Aerospace and Sciences, June 25-27, 2008, Genova, Italy
  22. Gorkem, E., G. Pillonetto, S. Carpin. Online estimation of variance parameters: experimental results with applications to localization, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Settembre 2008 Nice, France
  23. Pillonetto, G., A. Chiuso and G. De Nicolao. Predictor estimation via Gaussian regression, Proceedings of the 47th IEEE International Conference on Decision and Control, 2008 Cancun, Messico
  24. Chiuso, A., G. Pillonetto and G. De Nicolao. Subspace identification using predictor estimation via Gaussian regression, Proceedings of the 47th IEEE International Conference on Decision and Control, 2008 Cancun, Messico
  25. Ha Quang, M., G. Pillonetto and A. Chiuso. Nonlinear System Identification Via Gaussian Regression and Mixtures of Kernels, Proceedings of the 15th IFAC Symposium on System Identification, SYSID 2009, Saint-Malo, France, July 6-8, 2009
  26. Pillonetto, G. and A. Chiuso. Gaussian Processes for Wiener-Hammerstein System Identification, Proceedings of the 15th IFAC Symposium on System Identification, SYSID 2009, Saint-Malo, France, July 6-8, 2009
  27. Bell, B. and G. Pillonetto. A distributed Kalman smoother, Proceedings of the 1st IFAC Workshop on Estimation and Control of Networked Systems - NecSys'09, 2009 Venice, Italy
  28. Varagnolo, D., G. Pillonetto and L. Schenato. Distributed Function and Time Delay Estimation using Nonparametric Techniques, Proceedings of the 48th IEEE International Conference on Decision and Control, 2009 Shangai, China
  29. Pillonetto, G. and A. Chiuso. A Bayesian learning approach to linear system identification with missing data, Proceedings of the 48th IEEE International Conference on Decision and Control, 2009 Shangai, China
  30. Varagnolo D., and G. Pillonetto and L. Schenato. Distributed consensus-based Bayesian estimation: sufficient conditions for performance characterization, Proceedings of the 2010 American Control Conference
  31. Pillonetto, G. and A. Chiuso and G. De Nicolao. Regularized estimation of sums of exponentials in spaces generated by stable spline kernels, Proceedings of the 2010 American Control Conference
  32. Del Favero S., G. Pillonetto and B. Bell. Distributed Inequality Constrained Kalman Smoother, Proceedings of the 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2010)
  33. Pillonetto G., A. Aravkin and S. Carpin. The unconstrained and inequality constrained moving horizon approach to robot localization, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Settembre 2010 (to appear)
  34. Varagnolo, D., G. Pillonetto and L. Schenato. Distributed statistical estimation of the number of nodes in Sensor Networks, Proceedings of the 49th IEEE Conference on Decision and Control, 2010, Baltimora
  35. Chiuso, A. and G. Pillonetto. Nonparametric sparse estimators for identification of large scale linear systems, Proceedings of the 49th IEEE Conference on Decision and Control, 2010, Baltimora
  36. Chiuso, A. and G. Pillonetto. Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors, NIPS 2010, Vancouver
  37. Dinuzzo, F., C. S. Ong, P. Gehler, and G. Pillonetto. Learning output kernels with block coordinate descent. In International Conference on Machine Learning, Bellevue WA (USA), 2011.
  38. Aravkin, A., B. Bell, J. Burke and G. Pillonetto, Learning Using State Space Kernel Machines, IFAC WC 2011, Milano.
  39. Del Favero, S., G. Pillonetto, C. Cobelli and G. De Nicolao, A novel nonparametric approach for the identification of the glucose-insulin system in Type 1 diabetic patients, IFAC WC 2011, Milano.
  40. Aravkin, A., J. Burke, A. Chiuso, and G. Pillonetto. Convex vs nonconvex approaches for sparse estimation: Lasso, Multiple Kernel Learning and Hyperparameter Lasso. IEEE CDC 2011, 2011
  41. Zanella, F., D. Varagnolo, A. Cenedese, G. Pillonetto, and L. Schenato. Newton-Raphson consensus for distributed convex optimization. IEEE Conference on Decision and Control (CDC 2011), 2011
  42. Del Favero, S., D. Varagnolo, F. Dinuzzo, L. Schenato, and G. Pillonetto. On the discardability of data in Support Vector Classification problems. IEEE Conference on Decision and Control (CDC 2011), 2011
  43. Pillonetto, G. and G. De Nicolao. Kernel selection in linear system identification. Part I: a Gaussian process perspective. IEEE Conference on Decision and Control (CDC 2011), 2011
  44. Bottegal, G. and G. Pillonetto. Regularized spectrum estimation in spaces induced by stable spline kernels, Proceedings of the 2012 American Control Conference, Montreal, Canada, 2012
  45. Varagnolo, D., G. Pillonetto and L. Schenato. Distributed estimation of the size of an anonymous network using Bernoulli trials, Proceedings of the 2012 American Control Conference, Montreal, Canada, 2012
  46. Zanella, F., D. Varagnolo, A. Cenedese, G. Pillonetto and L. Schenato. Multidimensional Newton-Raphson consensus for distributed convex optimization, Proceedings of the 2012 American Control Conference, Montreal, Canada, 2012
  47. Pillonetto, G. and G. De Nicolao. Pitfalls of the parametric approaches exploiting cross-validation or model order selection . IFAC Symposium on System Identification (SysId 2012), Brussels, 2012
  48. F.P. Carli, A. Chiuso and G. Pillonetto. Efficient algorithms for large scale linear system identification using stable spline estimators. IFAC Symposium on System Identification (SysId 2012), Brussels, 2012
  49. Aravkin, A., J. Burke, A. Chiuso, and G. Pillonetto. On the estimation of hyperparameters for Empirical Bayes estimators: Maximum Marginal Likelihood vs Minimum MSE . IFAC Symposium on System Identification (SysId 2012), Brussels, 2012
  50. Aravkin, A., J. Burke, A. Chiuso, and G. Pillonetto. On the MSE Properties of Empirical Bayes Methods for Sparse Estimation. IFAC Symposium on System Identification (SysId 2012), Brussels, 2012
  51. Aravkin, A., J. Burke and G. Pillonetto. Robust and Trend-following Kalman Smoothers using Student's t. IFAC Symposium on System Identification (SysId 2012), Brussels, 2012
  52. Aravkin, A., J. Burke and G. Pillonetto. A statistical and computational theory for robust and sparse Kalman smoothing. IFAC Symposium on System Identification (SysId 2012), Brussels, 2012
  53. Zanella, F., D. Varagnolo, A. Cenedese, G. Pillonetto and L. Schenato. Asynchronous Newton-Raphson Consensus for Distributed Convex Optimization. Proceedings of the 3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys 2012), Santa Barbara, 2012
  54. Zanella, F., D. Varagnolo, A. Cenedese, G. Pillonetto and L. Schenato The convergence rate of Newton-Raphson consensus optimization for quadratic cost functions. IEEE Conference on Decision and Control (CDC 2012), 2012
  55. Del Favero, S., D. Varagnolo and G. Pillonetto Bayesian learning of probability density functions: a Markov chain Monte Carlo approach. IEEE Conference on Decision and Control (CDC 2012), 2012
  56. Aravkin, A.Y., J.V. Burke and G. Pillonetto Nonsmooth regression and state estimation using piecewise quadratic log-concave densities. IEEE Conference on Decision and Control (CDC 2012), 2012
  57. Carli, F.P., T. Chen, A. Chiuso, L. Ljung and G. Pillonetto On the estimation of hyperparameters for Bayesian system identification with exponential kernels. IEEE Conference on Decision and Control (CDC 2012), 2012
  58. Chen, T., L. Ljung, M. Andersen, A. Chiuso, F.P. Carli and G. Pillonetto Sparse multiple kernels for impulse response estimation with majorization minimization algorithms. IEEE Conference on Decision and Control (CDC 2012), 2012
  59. Pillonetto, G. , T. Chen and L. Ljung. Kernel-based model order selection for linear system identification, Proceedings of IFAC International Workshop on Adaptation and Learning in Control and Signal Processing (ALCOSP 2013), Caen (France) 2013
  60. Pillonetto, G. , T. Chen and L. Ljung. Kernel-based model order selection for identification and prediction of linear dynamic systems, IEEE Conference on Decision and Control (CDC 2013), 2013
  61. Chen, T., A. Chiuso, G. Pillonetto and L. Ljung. Rank-1 kernels for regularized system identification, IEEE Conference on Decision and Control (CDC 2013), 2013
  62. Aravkin, A.Y., J.V. Burke and G. Pillonetto. Linear system identification using stable spline kernels and PLQ penalties, IEEE Conference on Decision and Control (CDC 2013), 2013
  63. Chiuso, A., T. Chen, L. Ljung and G. Pillonetto. Regularization strategies for nonparametric system identification, IEEE Conference on Decision and Control (CDC 2013), 2013
  64. Bottegal, G., A. Aravkin, H. Hjalmarsson and G. Pillonetto. Outlier robust system identification: a Bayesian kernel-based approach, Proceedings of the 19th IFAC World Congress, 2014
  65. Chiuso, A. and G. Pillonetto. Bayesian and nonparametric methods for system identification and model selection, Proceedings of the 13th European Control Conference, Strasbourg, 2014
  66. Pillonetto, G. and A. Chiuso. Tuning complexity in kernel-based linear system identification: the robustness of the marginal likelihood estimator, Proceedings of the 13th European Control Conference, Strasbourg, 2014
  67. Pillonetto, G. and A. Aravkin. A New Kernel-Based Approach To Identification Of Time-Varying Linear Systems. Proceedings of the IEEE Machine Learning for Signal Processing Workshop, Reims, 2014
  68. Aravkin, A., K. Ramamurthy and G. Pillonetto. Kalman smoothing with persistent nuisance parameters. Proceedings of the IEEE Machine Learning for Signal Processing Workshop, Reims, 2014