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Articles in Journals
A. Carč,
E. Weyer, B.C.
Csaji, M.C. Campi,
Signed-Perturbed Sums Estimation of ARX
Systems: Exact Coverage and Strong Consistency.
SIAM J. Control Optimization, vol. 63, pp. 1902-1928,
2025.
S. Garatti, M.C. Campi
Non-convex scenario optimization
Matheamtical Programming, published online, 2024. https://doi.org/10.1007/s10107-024-02074-3M.C. Campi, S. Garatti
Compression, Generalization and Learning.
Journal of Machine Learning Research, Vol. 24, 1-74, 2023.S. Garatti, M.C. Campi
On Conditional Risk Assessments in Scenario Optimization.
SIAM Journal on Optimization, Vol. 33(2), 455-480, 2023.M.C. Campi
Inductive knowledge under dominance.
Synthese, Vol. 201(6), article 184, 2023.S. Garatti, A. Carč, M.C. Campi
Complexity is an effective observable to tune early stopping in scenario optimization.
IEEE Trans. on Automatic Control, Vol. 68(2), 928-942, 2023.
M.C. Campi, A. Carč, S. Garatti.
The
scenario approach: A
tool at the service of data-driven decision
making.
Annual Reviews in
Control,
52, pp.1-17, 2021.
G. Arici, M.C.
Campi, A. Carč, M. Dalai, F.A.
Ramponi.
A
Theory of the Risk for Empirical CVaR
with
Application to Portfolio Selection.
J.
Syst. Sci. Complexity
(special issue on occasion of the 60th birthday
of Professor
Lei Guo), vol. 34(5), 1879-1894, 2021.
M.C. Campi, S. Garatti
A Theory of the Risk for Optimization with
Relaxation and its Application to Support Vector
Machines.
A. Carč,
M.C. Campi, E. Weyer.
State
Conditional
Filtering.
IEEE Trans. on
Automatic
Control, Vol. 67(7), 3381-3395.
A. Carč,
M.C. Campi, B.C. Csaji, E. Weyer.
Facing
undermodelling in Sign-Perturbed-Sums
system identification.
Systems & Control Letters, vol. 153, 2021
(https://doi.org/10.1016/j.sysconle.2021.104936).
A.T.J.R. Cobbenhagen, A. Carč,
M.C. Campi, F.A. Ramponi,
D.J.
Antunes,
W.P.M.H. Heemels
Novel bounds on the
probability of misclassificaiton
in
majority voting: leveraging the
majority size.
IEEE Control Systems Letters, vol. 5, issue 5, 1513 -
1518, 2020.
S. Garatti and M.C.
Campi.
Risk and complexity in
scenario optimization.
Mathematical Programming, 1-37, 2019.
S. Garatti, M.C. Campi, A. Carč,
On a Class of
Interval Predictor
Models with Universal Reliability.
Automatica,
110,
pp.1-9, 2019.
S. Formentin, M.C. Campi, A. Carč,
S.M. Savaresi.
Deterministic continuous-time
Virtual Reference
Feedback Tuning (VRFT) with application to PID design.
Systems & Control Letters, vol. 127, pp. 25-34,
2019.
S.Yu. Pilyugin,
M.C. Campi.
Opinion
formation in voting processes under
bounded confidence.
Networks
& Heterogeneous Media, vol. 14, no. 3, 617-632,
2019.
A. Carč, S. Garatti,
M.C. Campi.
The wait-and-judge scenario
approach applied to
antenna array design.
Computational
Management
Science, vol.16,
pp. 481-499,
2019.
M.S. Modarresi, Le Xie,
M.C. Campi, S. Garatti, A. Carč, A.A. Thatte,
P. R.
Kumar.
Scenario-based Economic Dispatch
with Tunable
Risk Levels in High-renewable Power Systems.
IEEE Trans. on Power Systems, Vol. 34(6), 5103-5114,
2019.
DOI: 10.1109/TPWRS.2018.2874464
A. Carč, F.A. Ramponi,
M.C. Campi.
A new classification algorithm
with
guaranteed sensitivity and specificity for medical
applications.
IEEE Control Systems Letters, vol. 2, issue 3, 393 -
398, 2018.
M.C.
Campi,
S. Garatti,
F.A. Ramponi
A General Scenario Theory for
Nonconvex
Optimization and Decision making.
IEEE Trans. on Automatic Control, AC-63, 4067-4078, 2018.
M.C.
Campi
and S. Garatti.
Wait-and-Judge Scenario
Optimization.
Mathematical Programming, 167(1),
155-189, 2018.
F.A. Ramponi,
M.C. Campi.
Expected shortfall: Heuristic and
certificates.
European Journal of Operational Research, 267,
1003-1013, 2018.
E. Weyer, M.C. Campi,
B.C. Csaji,
Asymptotic
properties of SPS confidence
regions.
Automatica, Vol. 82, pp. 287-294, 2017.
H. Ming, L. Xie,
M.C. Campi, S. Garatti, P.
R. Kumar.
Scenario-based Economic
Dispatch with Uncertain
Demand Response.
IEEE Trans. on Smart Grid, 2017.
DOI: 10.1109/TSG.2017.2778688.
A. Carč, B.C. Csaji,
M.C.
Campi and E. Weyer.
Finite-Sample
System Identification:
An overview and a new correltation
method.
IEEE Control Systems Letters, DOI 10.1109/LCSYS.2017.2720969,Online
ISSN:
2475-1456 , 2017.
A. Caré,
S. Garatti and M.C. Campi.
A Coverage
Theory for Least Squares.
Journal of the Royal
Statistical Society B, 79, Part 5, pp. 1367–1389, 2017.
S. Ko, E. Weyer and M.C.
Campi,
Non-asymptotic
model
quality assessment of transfer functions at multiple
frequency
points.
Automatica,
60:65-78, 2015.
A. Caré, S. Garatti
and
M.C. Campi.
Scenario min-max
optimization and the risk of empirical costs.
SIAM Journal on
Optimization, 25,
no.4: 2061-2080, 2015.
(article winner of the 2016 stochastic programming student
paper prize)
B.C. Csaji, M.C. Campi and E. Weyer.
Sign-Perturbed
Sums: A New System Identificaiton
Approach for Contructing
Exact
Non-Asymptotic
Confidence Regions in Linear Regression Models.
IEEE Trans. on Signal Processing, 63, no.1: 169-181,
2015.
S. Ko, E. Weyer and M.C.
Campi.
Non-asymptotic
model
quality assessment of
transfer functions at multiple frequency points.
Automatica,
60:65-78, 2015.
A. Caré,
S. Garatti and M.C. Campi.
FAST - Fast
Algorithm for the Scenario Technique.
Operations
Research, 62,
no.3: 662-671, 2014.
M.C. Campi and A. Carč.
Random convex programs with
L1-regularization:
sparsity and generalization.
SIAM Journal on
Control and
Optimization, 51,
no.5:
3532-3557, 2013.
S. Garatti and M.C. Campi.
Modulating
Robustness in Control Design:
Principles and Algorithms.
IEEE Control
Systems Magazine,
33: 36–51, 2013.
B.K. Pagnoncelli, D. Reich and
M.C. Campi.
Risk-Return
Trade-off
with the Scenario
Approach in Practice: A Case Study in Portfolio
Selection.
J. of Optimization Theory and Applications, 155:
707-722, 2012.
M.C. Campi and S. Garatti.
A Sampling-and-Discarding
Approach to
Chance-Constrained Optimization: Feasibility and
Optimality.
J. of Optimization
Theory and
Applications, 148:
257–280,
2011.
M.C. Campi.
European
Journal of
Control, 5:419-430,
2010.
M.C. Campi.
discussion on paper: "Why
is resorting to fate
wise? A critical look at randomized algorithms in
systems and control"
- Final comments by the author.
European
Journal of
Control, 5:440-441,
2010.
M.C. Campi.
Machine
Learning,
80:63-84, 2010.
M.C. Campi and E. Weyer.
IEEE Trans.
on
Automatic Control,
AC-55,
2708-2720, 2010.
S Garatti,
S.
Bittanti and M.C. Campi.
Iterative robust control:
speeding up
improvement through iterations.
Syst. Contr. Lett.,
59:139-146, 2010.
M.C. Campi, S. Garatti and M.
Prandini.
The Scenario
Approach for Systems and Control
Design.
Annual Reviews in Control, 33:149-157, 2009.
M.C. Campi, S. Ko and E. Weyer.
Non-asymptotic confidence
regions for model
parameters in the presence of unmodelled dynamics.
Automatica,
45:2175-2186, 2009.
M.C. Campi, G. Calafiore and S. Garatti.
Interval Predictor Models:
Identification and
Reliability.
Automatica, 45:382-392, 2009.
M.C. Campi and G. Calafiore.
Notes on the scenario
design approch.
IEEE Trans. on Automatic Control, AC-54, 382-385,
2009.
M.C. Campi and S. Garatti.
The Exact Feasibility of
Randomized Solutions
of Uncertain Convex Programs.
SIAM Journal on
Optimization, 19,
no.3: 1211-1230, 2008.
M.C. Campi, T. Sugie and F.
Sakai
An iterative identification
method for
linear continuous-time systems.
IEEE Trans. on
Automatic
Control,
AC-53:1661-1669, 2008.
M. Dalai, E. Weyer and M.C.
Campi.
Parameter
Identification
for Nonlinear Systems:
Guaranteed Confidence regions through LSCR.
Automatica, 43:1418-1425,
2007.
S. Bittanti and M.C. Campi.
Adaptive
Control of Linear Time Invariant
Systems: The ``Bet On the Best" Principle.
Communications in Information and Systems, 6:299-320,
2006.
T. Katayama, T. McKelvey, A. Sano, C. Cassandras and M.C.
Campi.
Trends in
systems and signals.
Annual Reviews in Control, 30:5-17, 2006.
G. Calafiore and M.C. Campi.
The Scenario Approach to
Robust Control Design.
IEEE Trans. on Automatic Control, AC-51:742-753,
2006.
M.C. Campi and S.M. Savaresi.
Direct nonlinear control
design: the Virtual
Reference Feedback Tuning (VRFT) approach.
IEEE Trans. on Automatic Control, AC-51:14-27, 2006.
S. Garatti, M.C. Campi and S. Bittanti.
The
asymptotic model quality assessment for
instrumental variable
identification revisited.
Syst. Contr. Lett., 55:494-500, 2006.
M.C. Campi and E. Weyer.
Guaranteed
non-asymptotic
confidence regions in
system identification.
Automatica,
41:1751-1764, 2005.
(the downloadable file is an extended version (with all
proofs) of the Automatica
paper)
G. Calafiore and M.C. Campi.
Uncertain convex programs:
randomized solutions
and confidence levels.
Mathematical Programming, 102, no.1: 25-46, 2005.
Electronic version made available by SPRINGER at: DOI
10.1007/s10107-003-0499-y
M.C. Campi, Su
Ki Ooi and E. Weyer.
Non-asymptotic
quality
assessment of
generalised FIR models with
periodic inputs.
Automatica, 40:2029-2041,
2004.
S. Garatti,
M.C. Campi and S. Bittanti.
Assessing the
quality of identified
models through the asymptotic theory - When is the
result reliable?
Automatica, 40:1319-1332, 2004.
M.C. Campi, J.P. Hespanha and M.
Prandini.
Cautious hierarchical switching
control of
stochastic linear systems.
Int. J. Adapt. Control and Signal Process., invited
paper for
the
Special Issue on "New Approaches to Adaptive Control",
18:319-333,
2004.
G. Calafiore and M.C. Campi.
A learning theory approach to the
construction
of predictor models.
Discrete and Continuous Dynamical Systems (ISSN
1078-0947),
156:166,
supplement volume 2003.
M.C. Campi, A. Lecchini
and S.M. Savaresi.
An application of the virtual
reference
feedback tuning method to a benchmark problem.
European Journal of Control, Special Issue on
"Design and Optimisation of
Restricted Complexity
Controllers",
1:66-76, 2003.
M.C. Campi and M. Prandini.
Randomized
algorithms for the
synthesis of cautious adaptive controllers.
Syst. Contr. Lett., invited paper for the Special
Issue on
"Adaptive Systems", 49:21-36, 2003.
A. Lecchini, M.C. Campi and S.M. Savaresi.
Virtual Reference Feedback Tuning
for two degree
of freedom controllers.
Int. J. Adapt. Control and Signal Process., invited
paper for
the
Special Issue on "New Ideas in Recursive Estimation and
stochastic
Adaptive Control", 16:355-371, 2002.
M.C. Campi and E. Weyer.
Finite sample
properties of system
identification methods.
IEEE Trans. on Automatic Control, AC-47:1329-1334,
2002.
E. Weyer
and M.C. Campi.
Non-asymptotic
confidence
ellipsoids
for the least squares estimate.
Automatica, 38:1529-1547,
2002.
M. C. Campi, A. Lecchini and S.M. Savaresi.
Virtual Reference Feedback Tuning:
a Direct
Method for the Design of Feedback Controllers.
Automatica, 38:1337-1346,
2002.
M.C. Campi and M.
Vidyasagar.
Learning with
prior information.
IEEE Trans. on Automatic Control, AC-46:1682-1695,
2001.
M.Prandini and M.C. Campi.
Adaptive LQG control of
input-output systems -
A cost-biased approach.
SIAM J. Control and Optim.,
39, no
5:
1499-1519, 2001.
S. Bittanti
and M.C. Campi.
Persistence of
excitation properties
for time-varying autoregressive systems.
SIAM J. Control and Optim.,
39,
no.1:133-140,
2001.
S. Bittanti, P. Bolzern, M.C.
Campi, A.
De Marco,
G. Poncia and W. Prandoni.
Modelling of a fluidised
bed combustors with char mass estimation purposes.
IEEE Trans. on Control Systems technology,
CST-8:247--256, 2000.
M.C. Campi and P.R. Kumar.
Adaptive Linear
Quadratic Gaussian
control: the cost-biased approach revisited.
SIAM J. Control and Optim.,
36,
no.6:1890-1907, 1998.
M.C. Campi and P.R. Kumar.
Learning
dynamical systems
in a
stationary environment.
Syst. Contr. Lett., Special Issue on "Learning
Theory",
34:125-132, 1998.
M. Prandini and M.C. Campi.
A new recursive identification
algorithm for
singularity free adaptive control.
Syst. Contr.
Lett.,
34:177-183,
1998.
M. Prandini, S. Bittanti and M.C. Campi.
A penalized
identification criterion
for securing controllability in adaptive control.
J. of Mathematical Systems, Estimation and Control.
Summary in
vol.8,
no.4: 491-494, retrieval code for full electronic
manuscript: 29460,
1998.
M.C. Campi.
A unique representation theorem for the conditional
expectation of
stationary processes and application to dynamic estimation
problems.
J. of Applied Probability, 34:372-380, 1997.
M.C. Campi.
Performance of RLS identification
algoritms
with forgetting factor: a $\phi$-mixing approach.
J. of Mathematical Systems, Estimation and Control,
7:29-53,
1997.
S. Bittanti,
M.C. Campi, and S. Savaresi.
Unbiased
estimation of a sinusoid in
colored noise via adapted notch filters.
Automatica, 33:209-215,
1997.
M. Prandini, M.C. Campi,
and R. Leonardi.
Optimal delay estimation and
performance
evaluation in blind equalization.
Int. J. Adapt. Control and Signal Process.,
11:621-640, 1997.
M.C. Campi and M. James.
Nonlinear discrete-time
risk-sensitive
optimal control.
Int. J. Robust and Nonlinear Control, 6:1-19, 1996.
M.C. Campi.
The problem of
pole-zero
cancellation in transfer function identification and
application to
adaptive
stabilization.
Automatica, 32:849-857,
1996.
M.C. Campi.
Adaptive control of non-minimum phase systems.
Int. J. Adapt. Control and Signal Process.,
9:137-149, 1995.
M.C. Campi.
Exponentially
weighted least squares
identification of time-varying systems with white
disturbances.
IEEE Trans. on Signal Processing, SP-42:2906-2914,
1994.
M.C. Campi.
Reflections on "adaptive prediction".
J. of the Franklin Institute, 331B:251-264, 1994.
S. Bittanti and M.C. Campi.
Bounded error identification of
time-varying parameters by RLS techniques.
IEEE Trans. on Automatic Control, AC-39:1106-1110,
1994.
M.C. Campi, W.S. Lee, and
B.D.O. Anderson.
New filters for internal model
control design.
Int. J. Robust and Nonlinear Control, 4:757-775,
1994.
S. Bittanti, M.C. Campi and F. Lorito.
Effective identification
algorithms for adaptive control.
Int. J. Adapt. Control and Signal Process.,
6:221-235, 1992.
S.Bittanti and M.C. Campi.
Some remarks on self-tuning
controllers based on least squares and modified least
squares: a
comprehensive
approach.
Technical Cybernetics, 6:48-54, 1992 (in Russian).
M.C. Campi.
On the convergence of minimum-variance
directional-forgetting
adaptive
control schemes.
Automatica, 28:221-225,
1991.
S. Bittanti
and M.C. Campi.
Adaptive RLS
algorithms under
stochastic excitation - $l^2$ convergence analysis.
IEEE Trans. on Automatic Control, AC-36:963-967,
1991.
S. Bittanti and M.C. Campi.
Adaptive RLS algorithms
under stochastic excitation - strong consistency analysis.
Syst. Contr. Lett., 17:3-8, 1991.
S. Bittanti,
P. Bolzern, and M.C. Campi.
Recursive least squares
identification algorithms
with incomplete excitation: convergence analysis and
application to
adaptive
control.
IEEE Trans. on Automatic Control, AC-35:1371-1373,
1990.
S. Bittanti,
P. Bolzern, and M.C. Campi.
Convergence and exponential convergence of identification
algorithms
with
directional forgetting factor.
Automatica, 26:929-932,
1990.
S. Bittanti,
P. Bolzern, and M.C. Campi.
Exponential convergence of a modified directional
forgetting
identification
algorithm.
Syst. Contr. Lett., 14:131-137, 1990.
S. Bittanti,
P. Bolzern, and M.C. Campi.
A counterexample to the exponential convergence of the
directional
forgetting algorithm.
Int. J. Adapt. Control and Signal Process.,
4:237-244, 1990.
S. Bittanti,
P. Bolzern, and M.C. Campi.
Adaptive identification via prediction-error directional
forgetting
factor:
convergence analysis.
Int. J. Contr., 50:2407-2421, 1989.