Luigi Malag˛
Luigi Malag˛
Romanian Institute of Science and Technology - RIST, Cluj-Napoca, Romania
Verified email at - Homepage
Cited by
Cited by
Towards the geometry of estimation of distribution algorithms based on the exponential family
L Malag˛, M Matteucci, G Pistone
Proceedings of the 11th workshop proceedings on Foundations of geneticá…, 2011
Wasserstein Riemannian geometry of Gaussian densities
L Malag˛, L Montrucchio, G Pistone
Information Geometry 1 (2), 137-179, 2018
Information geometry of the Gaussian distribution in view of stochastic optimization
L Malag˛, G Pistone
Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithmsá…, 2015
Combinatorial optimization with information geometry: The newton method
L Malag˛, G Pistone
Entropy 16 (8), 4260-4289, 2014
Natural gradient, fitness modelling and model selection: A unifying perspective
L Malago, M Matteucci, G Pistone
2013 IEEE Congress on Evolutionary Computation, 486-493, 2013
Stochastic relaxation as a unifying approach in 0/1 programming
L Malago, M Matteucci, G Pistone
NIPS 2009 workshop on discrete optimization in machine learningá…, 2009
Online active learning with strong and weak annotators
L Malago, N Cesa-Bianchi, J Renders
NIPS Workshop on Learning from the Wisdom of Crowds, 2014
Introducing ℓ1-regularized logistic regression in Markov Networks based EDAs
M Luigi, M Matteucci, G Valentini
2011 IEEE Congress of Evolutionary Computation (CEC), 1581-1588, 2011
Stochastic Natural Gradient Descent by estimation of empirical covariances.
L Malago, M Matteucci, G Pistone
IEEE Congress on Evolutionary Computation, 949-956, 2011
A note on the border of an exponential family
L Malago, G Pistone
arXiv preprint arXiv:1012.0637, 2010
Evoptool: an extensible toolkit for evolutionary optimization algorithms comparison
G Valentini, L Malago, M Matteucci
IEEE Congress on Evolutionary Computation, 1-8, 2010
An information geometry perspective on estimation of distribution algorithms: boundary analysis
L Malag˛, M Matteucci, B Dal Seno
Proceedings of the 10th annual conference companion on Genetic andá…, 2008
Natural gradient flow in the mixture geometry of a discrete exponential family
L Malag˛, G Pistone
Entropy 17 (6), 4215-4254, 2015
Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks. arXiv e-prints, page
HJ Hortua, R Volpi, D Marinelli, L Malag˛
arXiv preprint arXiv:1911.08508, 2019
On the geometry of optimization based on the exponential family relaxation
L Malago
Italy, 2012
Milan RoboCup team 2009
A Bonarini, A Furlan, L Malago, D Marzorati, M Matteucci, D Migliore, ...
Proc of the RoboCup, 2009
Optimization by ℓ1-Constrained Markov Fitness Modelling
G Valentini, L Malag˛, M Matteucci
International Conference on Learning and Intelligent Optimization, 250-264, 2012
Gradient flow of the stochastic relaxation on a generic exponential family
L Malag˛, G Pistone
AIP Conference Proceedings 1641 (1), 353-360, 2015
Parameter estimation for the cosmic microwave background with Bayesian neural networks
HJ Hort˙a, R Volpi, D Marinelli, L Malag˛
Physical Review D 102 (10), 103509, 2020
Reliable Uncertainties for Bayesian Neural Networks using Alpha-divergences
HJ Hortua, L Malago, R Volpi
arXiv preprint arXiv:2008.06729, 2020
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