A Protocol for Continual Explanation of SHAP

Continual Learning trains models on a stream of data, with the aim of learning new information without forgetting previous knowledge. Given the dynamic nature of such environments, explaining the predictions of these models can be challenging. We study the behavior of SHAP values explanations in Continual Learning and propose an evaluation protocol to robustly assess the change of explanations in Class-Incremental scenarios. We observed that, while Replay strategies enforce the stability of SHAP values in feedforward/convolutional models, they are not able to do the same with fully-trained recurrent models. We show that alternative recurrent approaches, like randomized recurrent models, are more effective in keeping the explanations stable over time.Comment: ESANN 2023, 6 pages, added link to cod

A Bag of Receptive Fields for Time Series Extrinsic Predictions

High-dimensional time series data poses challenges due to its dynamic nature, varying lengths, and presence of missing values. This kind of data requires extensive preprocessing, limiting the applicability of existing Time Series Classification and Time Series Extrinsic Regression techniques. For this reason, we propose BORF, a Bag-Of-Receptive-Fields model, which incorporates notions from time series convolution and 1D-SAX to handle univariate and multivariate time series with varying lengths and missing values. We evaluate BORF on Time Series Classification and Time Series Extrinsic Regression tasks using the full UEA and UCR repositories, demonstrating its competitive performance against state-of-the-art methods. Finally, we outline how this representation can naturally provide saliency and feature-based explanations

Modeling Events and Interactions through Temporal Processes -- A Survey

In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these sequences of events. In this survey, we investigate probabilistic models for modeling event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that characterize the literature on the topic. We define an ontology to categorize the existing approaches in terms of three families: simple, marked, and spatio-temporal point processes. For each family, we systematically review the existing approaches based based on deep learning. Finally, we analyze the scenarios where the proposed techniques can be used for addressing prediction and modeling aspects.Comment: Image replacement

An overview of glioblastoma multiforme in vitro experimental models.

Glioblastoma multiforme (GBM) is the most common primary brain tumor, characterized by a remarkable inner complexity and inter-tumor variability. Moreover, it is very aggressive and resistant to conventional treatments, so that it rapidly relapse. Therefore, there is an immediate need for experimental strategies to enhance our comprehension of GBM, aiming to mitigate its economic and social impact. Here, we described different in vivo and in vitro strategies currently used for the study of GBM. First, we gave a brief and general overview of the classical in vivo models, including xenograft mouse and zebrafish models and canine models, offering a wide range of advantages but also presenting a series of strong limitations. Thus, we described in vitro models, starting from more traditional 2D culture models, comparing different approaches and critically exposing the advantages and disadvantages of using one or the other methods. We also briefly described GBM 2D culture systems that allow recreating multiple cell-cell and cell-extracellular matrix contacts but still do not reflect the complexity of in vivo tumors. We finally described the intricacies of the more novel 3D in vitro models, e.g., spheroids and organoids. These sophisticated models have demonstrated exceptional suitability across a wide spectrum of applications in cancer research, ranging from fundamental scientific inquiries to applications in translational research. Their adaptability and three-dimensional architecture render them invaluable tools, offering new insights and paving the way for advancements in both basic and applied researc

A Model Agnostic Local Explainer for Time Series Black-Box Classifiers

This study presents an agnostic approach to explain the predictions of time series black-box classifiers. Through an autoencoder, this method generates a local neighborhood of the instance to be explained, and then learns a local decision tree classifier to extract rules and counterfactuals. The final explanation is composed by exemplar and counterexemplar time series, and by a shapelet based verbose and graphical decision rule clarifying the black-box decision

A Protocol for Continual Explanation of SHAP

Continual Learning trains models on a stream of data, with the aim of learning new information without forgetting previous knowledge. Given the dynamic nature of such environments, explaining the predictions of these models can be challenging. We study the behavior of SHAP values explanations in Continual Learning and propose an evaluation protocol to robustly assess the change of explanations in Class-Incremental scenarios. We observed that, while Replay strategies enforce the stability of SHAP values in feedforward/convolutional models, they are not able to do the same with fully-trained recurrent models. We show that alternative recurrent approaches, like randomized recurrent models, are more effective in keeping the explanations stable over time

Explainable AI for Time Series Classification : A Review, Taxonomy and Research Directions

Time series data is increasingly used in a wide range of fields, and it is often relied on in crucial applications and high-stakes decision-making. For instance, sensors generate time series data to recognize different types of anomalies through automatic decision-making systems. Typically, these systems are realized with machine learning models that achieve top-tier performance on time series classification tasks. Unfortunately, the logic behind their prediction is opaque and hard to understand from a human standpoint. Recently, we observed a consistent increase in the development of explanation methods for time series classification justifying the need to structure and review the field. In this work, we (a) present the first extensive literature review on Explainable AI (XAI) for time series classification, (b) categorize the research field through a taxonomy subdividing the methods into time points-based, subsequences-based and instance-based, and (c) identify open research directions regarding the type of explanations and the evaluation of explanations and interpretability.publishe

The Noether Symmetry Approach: Foundation and Applications: The Case of Scalar-Tensor Gauss–Bonnet Gravity

We sketch the main features of the Noether Symmetry Approach, a method to reduce and solve dynamics of physical systems by selecting Noether symmetries, which correspond to conserved quantities. Specifically, we take into account the vanishing Lie derivative condition for general canonical Lagrangians to select symmetries. Furthermore, we extend the prescription to the first prolongation of the Noether vector. It is possible to show that the latter application provides a general constraint on the infinitesimal generator ξ, related to the spacetime translations. This approach can be used for several applications. In the second part of the work, we consider a gravity theory, including the coupling between a scalar field ϕ and the Gauss–Bonnet topological term G. In particular, we study a gravitational action containing the function F(G,ϕ) and select viable models by the existence of symmetries. Finally, we evaluate the selected models in a spatially flat cosmological background and use symmetries to find exact solutions