Author name cluster

Michele Donini

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9 papers

2 author rows

ICML Conference 2024 Conference Paper

Explaining Probabilistic Models with Distributional Values

Luca Franceschi 0001
Michele Donini
Cédric Archambeau
Matthias W. Seeger

A large branch of explainable machine learning is grounded in cooperative game theory. However, research indicates that game-theoretic explanations may mislead or be hard to interpret. We argue that often there is a critical mismatch between what one wishes to explain (e. g. the output of a classifier) and what current methods such as SHAP explain (e. g. the scalar probability of a class). This paper addresses such gap for probabilistic models by generalising cooperative games and value operators. We introduce the distributional values, random variables that track changes in the model output (e. g. flipping of the predicted class) and derive their analytic expressions for games with Gaussian, Bernoulli and Categorical payoffs. We further establish several characterising properties, and show that our framework provides fine-grained and insightful explanations with case studies on vision and language models.

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JMLR Journal 2024 Journal Article

Fortuna: A Library for Uncertainty Quantification in Deep Learning

Gianluca Detommaso
Alberto Gasparin
Michele Donini
Matthias Seeger
Andrew Gordon Wilson
Cedric Archambeau

We present Fortuna, an open-source library for uncertainty quantification in deep learning. Fortuna supports a range of calibration techniques, such as conformal prediction that can be applied to any trained neural network to generate reliable uncertainty estimates, and scalable Bayesian inference methods that can be applied to deep neural networks trained from scratch for improved uncertainty quantification and accuracy. By providing a coherent framework for advanced uncertainty quantification methods, Fortuna simplifies the process of benchmarking and helps practitioners build robust AI systems. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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AAMAS Conference 2022 Conference Paper

Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis

Cristina Cornelio
Michele Donini
Andrea Loreggia
Maria Silvia Pini
Francesca Rossi

Ensemble methods are built by training many different models and aggregating their outputs to output the prediction of the whole system. In this work, we study the behavior of an ensemble method where voting rules are used to aggregate the output of a set of randomly-generated classifiers. We provide both a theoretical and an empirical analysis of this method, showing that it performs comparably with other state-of-the-art ensemble methods, while not requiring any domain expertise to fine-tune the individual classifiers.

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JAAMAS Journal 2021 Journal Article

Voting with random classifiers (VORACE): theoretical and experimental analysis

Cristina Cornelio
Michele Donini
Francesca Rossi

Abstract In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.

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NeurIPS Conference 2020 Conference Paper

Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning

Luca Oneto
Michele Donini
Giulia Luise
Carlo Ciliberto
Andreas Maurer
Massimiliano Pontil

Developing learning methods which do not discriminate subgroups in the population is a central goal of algorithmic fairness. One way to reach this goal is by modifying the data representation in order to meet certain fairness constraints. In this work we measure fairness according to demographic parity. This requires the probability of the possible model decisions to be independent of the sensitive information. We argue that the goal of imposing demographic parity can be substantially facilitated within a multitask learning setting. We present a method for learning a shared fair representation across multiple tasks, by means of different new constraints based on MMD and Sinkhorn Divergences. We derive learning bounds establishing that the learned representation transfers well to novel tasks. We present experiments on three real world datasets, showing that the proposed method outperforms state-of-the-art approaches by a significant margin.

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IJCAI Conference 2020 Conference Paper

Marthe: Scheduling the Learning Rate Via Online Hypergradients

Michele Donini
Luca Franceschi
Orchid Majumder
Massimiliano Pontil
Paolo Frasconi

We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization, aiming at good generalization. We describe the structure of the gradient of a validation error w. r. t. the learning rate schedule -- the hypergradient. Based on this, we introduce MARTHE, a novel online algorithm guided by cheap approximations of the hypergradient that uses past information from the optimization trajectory to simulate future behaviour. It interpolates between two recent techniques, RTHO (Franceschi et al. , 2017) and HD (Baydin et al. 2018), and is able to produce learning rate schedules that are more stable leading to models that generalize better.

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YNIMG Journal 2019 Journal Article

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important

Michele Donini
João M. Monteiro
Massimiliano Pontil
Tim Hahn
Andreas J. Fallgatter
John Shawe-Taylor
Janaina Mourão-Miranda

Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and genetics characteristics, is potentially beneficial but presents challenges in terms of finding the best data representation for the different sources of information. Their simple combination usually does not provide an improvement if compared with using the best source alone. In this paper, we proposed a framework based on a recent multiple kernel learning algorithm called EasyMKL and we investigated the benefits of this approach for diagnosing two different mental health diseases. The well known Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset tackling the Alzheimer Disease (AD) patients versus healthy controls classification task, and a second dataset tackling the task of classifying an heterogeneous group of depressed patients versus healthy controls. We used EasyMKL to combine a huge amount of basic kernels alongside a feature selection methodology, pursuing an optimal and sparse solution to facilitate interpretability. Our results show that the proposed approach, called EasyMKLFS, outperforms baselines (e. g. SVM and SimpleMKL), state-of-the-art random forests (RF) and feature selection (FS) methods.

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NeurIPS Conference 2018 Conference Paper

Empirical Risk Minimization Under Fairness Constraints

Michele Donini
Luca Oneto
Shai Ben-David
John Shawe-Taylor
Massimiliano Pontil

We address the problem of algorithmic fairness: ensuring that sensitive information does not unfairly influence the outcome of a classifier. We present an approach based on empirical risk minimization, which incorporates a fairness constraint into the learning problem. It encourages the conditional risk of the learned classifier to be approximately constant with respect to the sensitive variable. We derive both risk and fairness bounds that support the statistical consistency of our methodology. We specify our approach to kernel methods and observe that the fairness requirement implies an orthogonality constraint which can be easily added to these methods. We further observe that for linear models the constraint translates into a simple data preprocessing step. Experiments indicate that the method is empirically effective and performs favorably against state-of-the-art approaches.

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ICML Conference 2017 Conference Paper

Forward and Reverse Gradient-Based Hyperparameter Optimization

Luca Franceschi 0001
Michele Donini
Paolo Frasconi
Massimiliano Pontil

We study two procedures (reverse-mode and forward-mode) for computing the gradient of the validation error with respect to the hyperparameters of any iterative learning algorithm such as stochastic gradient descent. These procedures mirror two ways of computing gradients for recurrent neural networks and have different trade-offs in terms of running time and space requirements. Our formulation of the reverse-mode procedure is linked to previous work by Maclaurin et al (2015) but does not require reversible dynamics. Additionally, we explore the use of constraints on the hyperparameters. The forward-mode procedure is suitable for real-time hyperparameter updates, which may significantly speedup hyperparameter optimization on large datasets. We present a series of experiments on image and phone classification tasks. In the second task, previous gradient-based approaches are prohibitive. We show that our real-time algorithm yields state-of-the-art results in affordable time.

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