Search Results for author: Pietro Michiardi

Found 28 papers, 8 papers with code

Toward Generative Data Augmentation for Traffic Classification

no code implementations21 Oct 2023 Chao Wang, Alessandro Finamore, Pietro Michiardi, Massimo Gallo, Dario Rossi

Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance.

Classification Data Augmentation +1

MINDE: Mutual Information Neural Diffusion Estimation

no code implementations13 Oct 2023 Giulio Franzese, Mustapha Bounoua, Pietro Michiardi

In this work we present a new method for the estimation of Mutual Information (MI) between random variables.

Multi-modal Latent Diffusion

no code implementations7 Jun 2023 Mustapha Bounoua, Giulio Franzese, Pietro Michiardi

Multi-modal data-sets are ubiquitous in modern applications, and multi-modal Variational Autoencoders are a popular family of models that aim to learn a joint representation of the different modalities.

How Much is Enough? A Study on Diffusion Times in Score-based Generative Models

no code implementations10 Jun 2022 Giulio Franzese, Simone Rossi, Lixuan Yang, Alessandro Finamore, Dario Rossi, Maurizio Filippone, Pietro Michiardi

Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data.

Safer Autonomous Driving in a Stochastic, Partially-Observable Environment by Hierarchical Contingency Planning

no code implementations13 Apr 2022 Ugo Lecerf, Christelle Yemdji-Tchassi, Pietro Michiardi

When learning to act in a stochastic, partially observable environment, an intelligent agent should be prepared to anticipate a change in its belief of the environment state, and be capable of adapting its actions on-the-fly to changing conditions.

Autonomous Driving Autonomous Navigation

Automatically Learning Fallback Strategies with Model-Free Reinforcement Learning in Safety-Critical Driving Scenarios

no code implementations11 Apr 2022 Ugo Lecerf, Christelle Yemdji-Tchassi, Sébastien Aubert, Pietro Michiardi

When learning to behave in a stochastic environment where safety is critical, such as driving a vehicle in traffic, it is natural for human drivers to plan fallback strategies as a backup to use if ever there is an unexpected change in the environment.

Autonomous Driving Reinforcement Learning (RL)

Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?

no code implementations4 Apr 2022 Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, Maria A. Zuluaga

In this work, we study the anomaly detection performance of sixteen conventional, machine learning-based and, deep neural network approaches on five real-world open datasets.

BIG-bench Machine Learning Time Series +2

Improved optimization strategies for deep Multi-Task Networks

no code implementations21 Sep 2021 Lucas Pascal, Pietro Michiardi, Xavier Bost, Benoit Huet, Maria A. Zuluaga

In Multi-Task Learning (MTL), it is a common practice to train multi-task networks by optimizing an objective function, which is a weighted average of the task-specific objective functions.

Multi-Task Learning

Revisiting the Effects of Stochasticity for Hamiltonian Samplers

no code implementations30 Jun 2021 Giulio Franzese, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi

We revisit the theoretical properties of Hamiltonian stochastic differential equations (SDES) for Bayesian posterior sampling, and we study the two types of errors that arise from numerical SDE simulation: the discretization error and the error due to noisy gradient estimates in the context of data subsampling.

Numerical Integration

Model Selection for Bayesian Autoencoders

no code implementations NeurIPS 2021 Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone

We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization.

Model Selection Representation Learning

Parametric Bootstrap Ensembles as Variational Inference

no code implementations pproximateinference AABI Symposium 2021 Dimitrios Milios, Pietro Michiardi, Maurizio Filippone

In this paper, we employ variational arguments to establish a connection between ensemble methods for Neural Networks and Bayesian inference.

Bayesian Inference Variational Inference

Sparse within Sparse Gaussian Processes using Neighbor Information

no code implementations10 Nov 2020 Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone

In this work, we address one limitation of sparse GPs, which is due to the challenge in dealing with a large number of inducing variables without imposing a special structure on the inducing inputs.

Gaussian Processes Variational Inference

An Identifiable Double VAE For Disentangled Representations

no code implementations19 Oct 2020 Graziano Mita, Maurizio Filippone, Pietro Michiardi

A large part of the literature on learning disentangled representations focuses on variational autoencoders (VAE).


USAD: UnSupervised Anomaly Detection on Multivariate Time Series

2 code implementations KDD 2020 Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, Maria A. Zuluaga

Through a feasibility study using Orange's proprietary data we have been able to validate Orange's requirements on scalability, stability, robustness, training speed and high performance.

Time Series Time Series Analysis +1

Maximum Roaming Multi-Task Learning

1 code implementation17 Jun 2020 Lucas Pascal, Pietro Michiardi, Xavier Bost, Benoit Huet, Maria A. Zuluaga

Multi-task learning has gained popularity due to the advantages it provides with respect to resource usage and performance.

Inductive Bias Multi-Task Learning

Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling

no code implementations9 Jun 2020 Giulio Franzese, Rosa Candela, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi

In this work we define a unified mathematical framework to deepen our understanding of the role of stochastic gradient (SG) noise on the behavior of Markov chain Monte Carlo sampling (SGMCMC) algorithms.

A Variational View on Bootstrap Ensembles as Bayesian Inference

no code implementations8 Jun 2020 Dimitrios Milios, Pietro Michiardi, Maurizio Filippone

In this paper, we employ variational arguments to establish a connection between ensemble methods for Neural Networks and Bayesian inference.

Bayesian Inference valid

Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting

2 code implementations16 Mar 2020 Rosa Candela, Pietro Michiardi, Maurizio Filippone, Maria A. Zuluaga

Accurate travel products price forecasting is a highly desired feature that allows customers to take informed decisions about purchases, and companies to build and offer attractive tour packages.


LIBRE: Learning Interpretable Boolean Rule Ensembles

no code implementations15 Nov 2019 Graziano Mita, Paolo Papotti, Maurizio Filippone, Pietro Michiardi

We present a novel method - LIBRE - to learn an interpretable classifier, which materializes as a set of Boolean rules.

Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD

no code implementations21 Oct 2019 Rosa Candela, Giulio Franzese, Maurizio Filippone, Pietro Michiardi

Large scale machine learning is increasingly relying on distributed optimization, whereby several machines contribute to the training process of a statistical model.

Distributed Optimization

A comparative evaluation of novelty detection algorithms for discrete sequences

no code implementations28 Feb 2019 Rémi Domingues, Pietro Michiardi, Jérémie Barlet, Maurizio Filippone

The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection.

Fraud Detection Intrusion Detection +1

Good Initializations of Variational Bayes for Deep Models

no code implementations18 Oct 2018 Simone Rossi, Pietro Michiardi, Maurizio Filippone

Stochastic variational inference is an established way to carry out approximate Bayesian inference for deep models.

Bayesian Inference General Classification +2

Calibrating Deep Convolutional Gaussian Processes

1 code implementation26 May 2018 Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone

The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the ability of these models to accurately quantify the uncertainty in their predictions.

Decision Making Decision Making Under Uncertainty +3

Flexible Scheduling of Distributed Analytic Applications

1 code implementation29 Nov 2016 Francesco Pace, Daniele Venzano, Damiano Carra, Pietro Michiardi

This work addresses the problem of scheduling user-defined analytic applications, which we define as high-level compositions of frameworks, their components, and the logic necessary to carry out work.

Distributed, Parallel, and Cluster Computing

Random Feature Expansions for Deep Gaussian Processes

1 code implementation ICML 2017 Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone

The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty.

Gaussian Processes Variational Inference

Measuring Password Strength: An Empirical Analysis

2 code implementations20 Jul 2009 Matteo Dell'Amico, Pietro Michiardi, Yves Roudier

We present an in-depth analysis on the strength of the almost 10, 000 passwords from users of an instant messaging server in Italy.

Cryptography and Security

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