Publications

Selected papers appear in bold

#### Submitted

**Lagrangian and Hamiltonian Mechanics for Probabilities on the Statistical Manifold**.

Natural Wake-Sleep Algorithm.

#### Papers in International Journals

Changing the Geometry of Representations: α-Embeddings for NLP Tasks.

Entropy 2021, 23, 287, Special Issue Information Geometry III, 2021

**Natural Alpha Embeddings**.

Information Geometry, Vol. 3, Issue 2, 2021. [arXiv:1912.02280]

Constraining the Reionization History using Bayesian Normalizing Flows.

Machine Learning: Science and Technology, 1 035014, 2020. [arXiv:1911.08508]

Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks.

Phys. Rev. D 102, 103509, 2020. [arXiv:1911.08508]

**Wasserstein Riemannian Geometry of Positive Definite Matrices**.

Information Geometry, Vol. 1, Issue 2, pp 137-179, 2018

Natural Gradient Flow in the Mixture Geometry of a Discrete Exponential Family.

Entropy 17(6), Special Issue on Information, Entropy and their Geometric Structures, 4215-4254, 2015

Combinatorial Optimization with Information Geometry: Newton Method.

Entropy 16(8), Special Issue on Information Geometry, pages 4260-4289, 2014.

#### Papers in Proceedings of International Conferences and Workshops

Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks.

Proceedings of the 5th Workshop on Representation Learning for NLP (RepL4NLP-2020), pages 61–71

Tumour Detection in Brain MRIs by Computing Dissimilarities in the Latent Space of a Variational AutoEncoder.

Proceedings of the Northern Lights Deep Learning Workshop (NLDL2020), Septentrio Academic Publishing, Vol 1, 2020.

Evaluating the Robustness of Defense Mechanisms based on AutoEncoder Reconstructions against Carlini-Wagner Adversarial Attacks.

Proceedings of the Northern Lights Deep Learning Workshop (NLDL2020), Septentrio Academic Publishing, Vol 1, 2020.

Second-Order Optimization over the Multivariate Gaussian Distribution.

Geometric Science of Information (GSI), 2015.

Information Geometry of Gaussian Distributions in View of Stochastic Optimization.

Proceedings of FOGA '15, held on January 17-20, 2015, Aberystwyth, Wales, 2015.

Gradient Flow of the Stochastic Relaxation on a Generic Exponential Family.

Proceedings of MaxEnt 2014, held on September 21-26, 2014, Château Clos Lucé, Amboise, France, 2014.

Extra material: [animation]

Optimization via Information Geometry.

Book of Proceedings of the Seventh International Workshop on Simulation (IWS) held on May 21-25, 2013, in Rimini, Italy, 2014.

Robust Estimation of Natural Gradient in Optimization by Regularized Linear Regression.

Geometric Science of Information (GSI), 2013.

**Natural Gradient, Fitness Modelling and Model Selection: A Unifying Perspective**.

IEEE Congress on Evolutionary Computation (CEC), 2013.

Variable Transformations in Estimation of Distribution Algorithms.

Parallel Problem Solving from Nature - PPSN XII, Lecture Notes in Computer Science Volume 7491, pages 428--437, 2012.

Implicit Model Selection based on Variable Transformations in Estimation of Distribution.

Learning and Intelligent OptimizatioN Conference LION 6, Lecture Notes in Computer Science, pages 360--365, 2012.

Optimization by l1-constrained Markov fitness modelling.

Learning and Intelligent OptimizatioN Conference LION 6, Lecture Notes in Computer Science, pages 250--264, 2012.

Optimization of pseudo-boolean functions by stochastic natural gradient descent.

9th Metaheuristics International Conference (MIC), 2011.

Introducing l1-regularized logistic regression in Markov networks based EDAs.

Evolutionary Computation (CEC), 2011 IEEE Congress on, pages 1581--1588, 2011.

Stochastic natural gradient descent by estimation of empirical covariances.

Evolutionary Computation (CEC), 2011 IEEE Congress on, pages 949--956, 2011.

**Towards the geometry of estimation of distribution algorithms based on the exponential family**.

Proceedings of FOGA '11, pages 230--242, New York, NY, USA, 2011. ACM.

Evoptool: An extensible toolkit for evolutionary optimization algorithms comparison.

Evolutionary Computation (CEC), 2010 IEEE Congress on, pages 1--8, 2010.

#### Workshop Papers

Natural Reweighted Wake-Sleep.

Deep Learning through Information Geometry NeurIPS Worksop, December 12, 2020

Accelerating MCMC algorithms through Bayesian Deep Networks.

In Machine Learning and the Physical Sciences Workshop at the 34th NeurIPS, December 11, 2020

Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations.

KDD Workshop on Applied Data Science for Healthcare, Trustable and Actionable AI for Healthcare, August 24, 2020

Detection of Tumours in Brain MRIs with Variational AutoEncoders.

Machine Learning for Pharma and Healthcare Applications ECML PKDD 2020 Workshop, September 14, 2020

Automatic Feature Extraction for Phonocardiogram Heartbeat Anomaly Detection using WaveNetVAE.

Machine Learning for Pharma and Healthcare Applications ECML PKDD 2020 Workshop, September 14, 2020

Reliable Uncertainties for Bayesian Neural Networks using Alpha-divergences.

Uncertainty & Robustness in Deep Learning 2020, ICML Workshop, July 17, 2020

Parameters Estimation from the 21 cm signal using Variational Inference.

ICLR 2020 Workshop on Fundamental Science in the era of AI, April 26 2020

Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds.

ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden, 14-15 July 2018.

An Explanatory Analysis of the Geometry of Latent Variables Learned by Variational Auto-Encoders.

NIPS 2017 Workshop on Bayesian Deep Learning, Long Beach, US, 9 December 2017.

Towards the Use of Gaussian Graphical Models in Variational Autoencoders.

ICML 2017 Workshop on Implicit Models, Sydney, Australia, 10 August 2017.

Synthetic Generation of Local Minima and Saddle Points for Neural Networks.

ICML 2017 Workskop on Principled Approaches to Deep Learning, Sydney, Australia, 10 August 2017.

**.**

Online Active Learning with Strong and Weak Annotators

Online Active Learning with Strong and Weak Annotators

NIPS 2014 Workshop on Crowdsourcing and Machine Learning, Montreal, Canada, 13 December 2014.

Stochastic Relaxation over the Exponential Family: Second-Order Geometry.

NIPS 2014 Workshop on Optimization for Machine Learning (OPT2014), Montreal, Canada, 12 December 2014.

A note on the border of an exponential family.

Working paper, Carlo Alberto Notebooks, No. 168. A shorter version of the paper was presented at the SIS 2010 conference in Padova, Italy. [arXiv:1012.0637]

Stochastic relaxation as a unifying approach in 0/1 programming.

NIPS 2009 Workshop on Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra (DISCML), Whistler, Canada, 2009.

An information geometry perspective on estimation of distribution algorithms: boundary analysis.

Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation, GECCO '08, pages 2081--2088, New York, NY, USA, 2008. ACM.

#### Other contributions

Milan Robocup Team 2009.

Robocup International Symposium 2009, Robocup 2009, Graz, Austria, pages 1--8, 2009.

IERoKi, Innovative Entertainment Robot for Kids.

In Multidisciplinarity and Innovation, ASP projects 1, Alta Scuola Politecnica, pages 56--59. Telesma Edizioni, 2007.

Extra material: [video]

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