| Wasserstein Riemannian geometry of Gaussian densities L Malagò, L Montrucchio, G Pistone Information Geometry 1, 137-179, 2018 | 123* | 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 | 78 | 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 | 55* | 2020 |
| 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 | 51 | 2011 |
| Constraining the reionization history using Bayesian normalizing flows HJ Hortúa, L Malagò, R Volpi Machine Learning: Science and Technology 1 (3), 035014, 2020 | 27 | 2020 |
| 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 | 22 | 2013 |
| Combinatorial optimization with information geometry: The Newton method L Malagò, G Pistone Entropy 16 (8), 4260-4289, 2014 | 17 | 2014 |
| 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 | 16 | 2014 |
| Natural gradient flow in the mixture geometry of a discrete exponential family L Malagò, G Pistone Entropy 17 (6), 4215-4254, 2015 | 14 | 2015 |
| 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 |
| Lagrangian and Hamiltonian dynamics for probabilities on the statistical bundle G Chirco, L Malagò, G Pistone International Journal of Geometric Methods in Modern Physics 19 (13), 2250214, 2022 | 13 | 2022 |
| 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 | 12 | 2011 |
| Stochastic natural gradient descent by estimation of empirical covariances M Luigi, M Matteo, G Pistone 2011 IEEE Congress of Evolutionary Computation (CEC), 949-956, 2011 | 12 | 2011 |
| A note on the border of an exponential family L Malago, G Pistone arXiv preprint arXiv:1012.0637 1, 2010 | 11 | 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 | 11 | 2008 |
| Evaluating the robustness of defense mechanisms based on autoencoder reconstructions against carlini-wagner adversarial attacks P Hlihor, R Volpi, L Malagò Proceedings of the Northern Lights Deep Learning Workshop 1, 6-6, 2020 | 9 | 2020 |
| Evoptool: an extensible toolkit for evolutionary optimization algorithms comparison G Valentini, L Malago, M Matteucci IEEE Congress on Evolutionary Computation, 1-8, 2010 | 9 | 2010 |
| Accelerating mcmc algorithms through bayesian deep networks HJ Hortua, R Volpi, D Marinelli, L Malago arXiv preprint arXiv:2011.14276, 2020 | 8 | 2020 |
| Natural wake-sleep algorithm C Várady, N Ay, R Volpi, L Malagò | 7 | 2020 |
| Evaluating natural alpha embeddings on intrinsic and extrinsic tasks R Volpi, L Malagò Proceedings of the 5th Workshop on Representation Learning for NLP, 61-71, 2020 | 6 | 2020 |
Giovanni Pistoneprofessor, de Castro Statistics Initiative, Collegio Carlo Alberto, TorinoVerified email at carloalberto.org