Accurately identifying items forgotten during a supermarket visit and providing clear, interpretable explanations for recommending them remains an underexplored problem within the Next Basket Prediction (NBP) domain. Existing NBP approaches typically only focus on forecasting future purchases, without explicitly addressing the detection of unintentionally omitted items. This gap is partly due to the scarcity of real-world datasets that allow for the reliable estimation of forgotten items. Furthermore, most current NBP methods rely on black-box models, which lack transparency and limit the ability to justify recommendations to end users. In this paper, we formally introduce the forgotten-item prediction task and propose two novel interpretable-by-design algorithms. These methods are tailored to identify forgotten items while offering intuitive, human-understandable explanations. Experiments on a real-world retail dataset show our algorithms outperform state-of-the-art NBP baselines by 10–15% across multiple evaluation metrics.

Representation learning is a central topic in machine learning, with significant efforts dedicated to encoding structured data such as sequences, trees, and graphs for various downstream tasks.A branch of these studies focuses on functional data analysis, which views data not as discrete arrays but as continuous functions.When these functions are parameterized using neural networks, they are called Implicit Neural Representations (INR).INRs have been successfully applied to represent diverse data types but, to the best of our knowledge, have not been used for encoding decision models.This work addresses the novel challenge of using INRs to represent decision trees.We introduce a tailored coordinate system and train INRs to reconstruct decision trees with a loss function to minimize node reconstruction errors.We benchmark implicit neural decision trees on several datasets, showing that they can effectively represent individual trees, and show potential extensions to tree forests through meta-learning.

Abstract Counterfactual explanations provide an intuitive way to understand model decisions by identifying minimal changes required to alter an outcome. However, applying counterfactual methods to time series models remains challenging due to temporal dependencies, high dimensionality, and the lack of an intuitive human-interpretable representation. We introduce MASCOTS, a method that leverages the Bag-of-Receptive-Fields representation alongside symbolic transformations inspired by Symbolic Aggregate Approximation. By operating in a symbolic feature space, it enhances interpretability while preserving fidelity to the original data and model. Unlike existing approaches that either depend on model structure or autoencoder-based sampling, MASCOTS directly generates meaningful and diverse counterfactual observations in a model-agnostic manner, operating on both univariate and multivariate data. We evaluate MASCOTS on univariate and multivariate benchmark datasets, demonstrating comparable validity, proximity, and plausibility to state-of-the-art methods, while significantly improving interpretability and sparsity. Its symbolic nature allows for explanations that can be expressed visually, in natural language, or through semantic representations, making counterfactual reasoning more accessible and actionable.

In real-world scenarios, numerous phenomena generate a series of events that occur in continuous time. Point processes provide a natural mathematical framework for modeling these event sequences. In this comprehensive survey, we aim to explore probabilistic models that capture the dynamics of event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that underpin the existing literature on this topic. To structure our survey effectively, we introduce an ontology that categorizes the existing approaches considering three horizontal axes: modeling , inference and estimation , and application . We conduct a systematic review of the existing approaches, with a particular focus on those leveraging deep learning techniques. Finally, we delve into the practical applications where these proposed techniques can be harnessed to address real-world problems related to event modeling. Additionally, we provide a selection of benchmark datasets that can be employed to validate the approaches for point processes.

Recurrent Neural Networks are effective for analyzing temporal data, such as time series, but they often require costly and time-intensive training.Echo State Networks simplify the training process by using a fixed recurrent layer, the reservoir, and a trainable output layer, the readout.In sequence classification problems, the readout typically receives only the final state of the reservoir.However, averaging all states can sometimes be beneficial.In this work, we assess whether a weighted average of hidden states can enhance the Echo State Network performance.To this end, we propose a gradient-based, explainable technique to guide the contribution of each hidden state towards the final prediction.We show that our approach outperforms the naive average, as well as other baselines, in time series classification, particularly on noisy data.

Continual Learning (CL) trains models on streams of data, with the aim of learning new information without forgetting previous knowledge. However, many of these models lack interpretability, making it difficult to understand or explain how they make decisions. This lack of interpretability becomes even more challenging given the non-stationary nature of the data streams in CL. Furthermore, CL strategies aimed at mitigating forgetting directly impact the learned representations. We study the behavior of different explanation methods in CL and propose CLEX (ContinuaL EXplanations), an evaluation protocol to robustly assess the change of explanations in Class-Incremental scenarios, where forgetting is pronounced. We observed that models with similar predictive accuracy do not generate similar explanations. Replay-based strategies, well-known to be some of the most effective ones in class-incremental scenarios, are able to generate explanations that are aligned to the ones of a model trained offline. On the contrary, naive fine-tuning often results in degenerate explanations that drift from the ones of an offline model. Finally, we discovered that even replay strategies do not always operate at best when applied to fully-trained recurrent models. Instead, randomized recurrent models (leveraging on an untrained recurrent component) clearly reduce the drift of the explanations. This discrepancy between fully-trained and randomized recurrent models, previously known only in the context of their predictive continual performance, is more general, including also continual explanations.

The growing availability of time series data has increased the usage of classifiers for this data type. Unfortunately, state-of-the-art time series classifiers are black-box models and, therefore, not usable in critical domains such as healthcare or finance, where explainability can be a crucial requirement. This paper presents a framework to explain the predictions of any black-box classifier for univariate and multivariate time series. The provided explanation is composed of three parts. First, a saliency map highlighting the most important parts of the time series for the classification. Second, an instance-based explanation exemplifies the black-box’s decision by providing a set of prototypical and counterfactual time series. Third, a factual and counterfactual rule-based explanation, revealing the reasons for the classification through logical conditions based on subsequences that must, or must not, be contained in the time series. Experiments and benchmarks show that the proposed method provides faithful, meaningful, stable, and interpretable explanations.

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.