| Towards the geometry of estimation of distribution algorithms based on the exponential family L Malagò, M Matteucci, G Pistone Proceedings of the 11th workshop proceedings on Foundations of genetic …, 2011 | 42 | 2011 |
| Wasserstein Riemannian geometry of Gaussian densities L Malagò, L Montrucchio, G Pistone Information Geometry 1 (2), 137-179, 2018 | 37* | 2018 |
| Information geometry of the Gaussian distribution in view of stochastic optimization L Malagò, G Pistone Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms …, 2015 | 23 | 2015 |
| Combinatorial optimization with information geometry: The newton method L Malagò, G Pistone Entropy 16 (8), 4260-4289, 2014 | 17 | 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 | 17 | 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 learning …, 2009 | 14 | 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 | 11 | 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 | 11 | 2011 |
| Stochastic Natural Gradient Descent by estimation of empirical covariances. L Malago, M Matteucci, G Pistone IEEE Congress on Evolutionary Computation, 949-956, 2011 | 11 | 2011 |
| A note on the border of an exponential family L Malago, G Pistone arXiv preprint arXiv:1012.0637, 2010 | 11 | 2010 |
| Evoptool: an extensible toolkit for evolutionary optimization algorithms comparison G Valentini, L Malago, M Matteucci IEEE Congress on Evolutionary Computation, 1-8, 2010 | 10 | 2010 |
| An information geometry perspective on estimation of distribution algorithms: boundary analysis L Malagò, M Matteucci, B Dal Seno Proceedings of the 10th annual conference companion on Genetic and …, 2008 | 10 | 2008 |
| Natural gradient flow in the mixture geometry of a discrete exponential family L Malagò, G Pistone Entropy 17 (6), 4215-4254, 2015 | 7 | 2015 |
| Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks. arXiv e-prints, page HJ Hortua, R Volpi, D Marinelli, L Malagò arXiv preprint arXiv:1911.08508, 2019 | 6 | 2019 |
| On the geometry of optimization based on the exponential family relaxation L Malago Italy, 2012 | 6 | 2012 |
| Milan RoboCup team 2009 A Bonarini, A Furlan, L Malago, D Marzorati, M Matteucci, D Migliore, ... Proc of the RoboCup, 2009 | 6 | 2009 |
| Optimization by ℓ1-Constrained Markov Fitness Modelling G Valentini, L Malagò, M Matteucci International Conference on Learning and Intelligent Optimization, 250-264, 2012 | 5 | 2012 |
| Gradient flow of the stochastic relaxation on a generic exponential family L Malagò, G Pistone AIP Conference Proceedings 1641 (1), 353-360, 2015 | 4 | 2015 |
| Parameter estimation for the cosmic microwave background with Bayesian neural networks HJ Hortúa, R Volpi, D Marinelli, L Malagò Physical Review D 102 (10), 103509, 2020 | 2 | 2020 |
| Reliable Uncertainties for Bayesian Neural Networks using Alpha-divergences HJ Hortua, L Malago, R Volpi arXiv preprint arXiv:2008.06729, 2020 | 2 | 2020 |
Giovanni Pistoneprofessor, de Castro Statistics Initiative, Collegio Carlo Alberto, TorinoVerified email at carloalberto.org
