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Daniele Alpago - IEEE Xplore Author Profile
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Daniele Alpago

Publications
6
Citations
69
Publications by Year
20182023
Co-Authors:
Show All Co-Authors (7)
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Daniele Alpago

Affiliation

Department of Information Engineering
University of Padova
Padova, Italy

Publication Topics

Biography

Daniele Alpago was born in Belluno, Italy, on September 15, 1993. He received the M.S. degree in automation engineering (cum laude), in 2017 from the University of Padova, Padova, Italy, where he is currently working toward the Ph.D degree in algorithms for graphical models selection and control of linear stochastic systems with the Department of Information Engineering.,His research interests include system identification, spectral estimation, and optimal mass transport.(Based on document published on 21 December 2021).
Publications
6
Citations
69
Publications by Year
20182023
Co-Authors:
Show All Co-Authors (7)
Author's Published Works

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Data-Driven Link Prediction Over Graphical Models

Daniele Alpago;Mattia Zorzi;Augusto Ferrante

IEEE Transactions on Automatic Control
Year: 2023 | Volume: 68, Issue: 4 | Journal Article |
Cited by: Papers (6)
The positive link prediction problem is formulated in a system identification framework: We consider dynamic graphical models for autoregressive moving-average (ARMA) Gaussian random processes. For the identification of the parameters, we model our network on two different time scales: A quicker one, over which we assume that the process representing the dynamics of the agents can be considered to...Show More

Data-Driven Link Prediction Over Graphical Models

Daniele Alpago;Mattia Zorzi;Augusto Ferrante

Year: 2023 | Volume: 68, Issue: 4 | Journal Article |

A Scalable Strategy for the Identification of Latent-Variable Graphical Models

Daniele Alpago;Mattia Zorzi;Augusto Ferrante

IEEE Transactions on Automatic Control
Year: 2022 | Volume: 67, Issue: 7 | Journal Article |
Cited by: Papers (8)
In this article, we propose an identification method for latent-variable graphical models associated with autoregressive (AR) Gaussian stationary processes. The identification procedure exploits the approximation of AR processes through stationary reciprocal processes thus benefiting of the numerical advantages of dealing with block-circulant matrices. These advantages become more and more signifi...Show More

A Scalable Strategy for the Identification of Latent-Variable Graphical Models

Daniele Alpago;Mattia Zorzi;Augusto Ferrante

Year: 2022 | Volume: 67, Issue: 7 | Journal Article |

Link Prediction: A Graphical Model Approach

Daniele Alpago;Mattia Zorzi;Augusto Ferrante

2020 European Control Conference (ECC)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
We consider the problem of link prediction in networks whose edge structure may vary (sufficiently slowly) over time. This problem, with applications in many important areas including social networks, has two main variants: the first, known as positive link prediction or PLP consists in estimating the appearance of a link in the network. The second, known as negative link prediction or NLP consist...Show More

Link Prediction: A Graphical Model Approach

Daniele Alpago;Mattia Zorzi;Augusto Ferrante

Year: 2020 | Conference Paper |

An Extended Kalman Filter for Data-Enabled Predictive Control

Daniele Alpago;Florian Dörfler;John Lygeros

IEEE Control Systems Letters
Year: 2020 | Volume: 4, Issue: 4 | Journal Article |
Cited by: Papers (27)
The literature dealing with data-driven analysis and control problems has significantly grown in the recent years. Most of the recent literature deals with linear time-invariant systems in which the uncertainty (if any) is assumed to be deterministic and bounded; relatively little attention has been devoted to stochastic linear time-invariant systems. As a first step in this direction, we propose ...Show More

An Extended Kalman Filter for Data-Enabled Predictive Control

Daniele Alpago;Florian Dörfler;John Lygeros

Year: 2020 | Volume: 4, Issue: 4 | Journal Article |

On optimal steering of a non-Markovian Gaussian process

Daniele Alpago;Yongxin Chen;Tryphon Georgiou;Michele Pavon

2019 IEEE 58th Conference on Decision and Control (CDC)
Year: 2019 | Conference Paper |
At present, the problem to steer general nonMarkovian processes between specified end-point marginal distributions with minimum energy remains unsolved. Herein, we consider the special case of a non-Markovian process y(t) which assumes a finite-dimensional stochastic realization with a Markov state process that is fully observable. In this setting, and over a finite time horizon [0, T], we determi...Show More

On optimal steering of a non-Markovian Gaussian process

Daniele Alpago;Yongxin Chen;Tryphon Georgiou;Michele Pavon

Year: 2019 | Conference Paper |

Identification of Sparse Reciprocal Graphical Models

Daniele Alpago;Mattia Zorzi;Augusto Ferrante

IEEE Control Systems Letters
Year: 2018 | Volume: 2, Issue: 4 | Journal Article |
Cited by: Papers (26)
In this letter we propose an identification procedure of a sparse graphical model associated to a Gaussian stationary stochastic process. The identification paradigm exploits the approximation of autoregressive (AR) processes through reciprocal processes in order to improve the robustness of the identification algorithm, especially when the order of the AR process becomes large. We show that the p...Show More

Identification of Sparse Reciprocal Graphical Models

Daniele Alpago;Mattia Zorzi;Augusto Ferrante

Year: 2018 | Volume: 2, Issue: 4 | Journal Article |

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