The authors describe a method for label distribution learning based on differentiable decision trees. The authors use differentiable sigmoid units to estimate a label distribution using leaf nodes of trees. Learning in split nodes is done via backprop. The authors compare their work with relevant methods on learning label distributions and show the competitiveness of their method. I think this is a good paper, providing a sound methodology for learning LD.

Effective management and early detection of BPSD are crucial to reduce the stress and burden on caregivers and healthcare systems. Despite the advancements in machine learning for dementia prediction, there is a considerable gap in utilizing these methods for BPSD prediction. This study aims to fill this gap by presenting a novel personalized framework for BPSD prediction, utilizing physiological signals from smart wearable devices. Our personalized fine-grained BPSD prediction method accurately predicts BPSD occurrences by extracting individual behavioral patterns, while the generalized models identify diverse patterns and differentiate between various BPSD symptoms. Detailed comparisons between the proposed personalized method and conventional generalized methods reveals substantial improvements across all performance metrics, including a 16.0% increase in AUC. These results demonstrate the potential of our proposed method in advancing dementia care by enabling proactive interventions and improving patient outcomes in real-world scenarios. To the best of our knowledge, this is the first study that leverages physiological signals from smart wearable devices to predict BPSD, marking a significant stride in dementia care research.

Unlike regular models, these very simple models--often referred to as toy models--are not required to be linked to the real world through structural similarity or resemblance relations. They are not meant to be approximations of the target world system, and in some cases, they are not even required to be representational. In semantic terms, they do not accurately map onto their targets. Despite these limitations, they are still useful in understanding theoretical concepts and possible configurations of the target system. Paradigmatic examples of toy models include Boyle's law and the Ising model in physics, the Lotka-Volterra model in population ecology, and the Schelling model in the social sciences (Weisberg, 2013). In recent years, philosophers of science have become interested in toy models (Grüne-Yanoff, 2009; Luczak, 2017; Reutlinger et al., 2018; Frigg & Nguyen, 2017; Nguyen, 2020). The main purpose of this literature is to explore the nature of these models and examine how they perform their epistemic function. Despite lacking the regular descriptive and predictive features of full-scale scientific models, they often offer an elementary understanding of a phenomenon. Their definitions of "toy model" differ as well as their assessment of the importance of representation in modelling generally, but they all agree that toy models play an important epistemic role in scientific research, exploration, and pedagogy.

In an era where artificial intelligence and machine learning algorithms increasingly impact human life, it is crucial to develop models that account for potential discrimination in their predictions. This paper tackles this problem by introducing a new classification tree algorithm using a novel splitting criterion that incorporates fairness adjustments into the tree-building process. The proposed method integrates a fairness-aware impurity measure that balances predictive accuracy with fairness across protected groups. By ensuring that each splitting node considers both the gain in classification error and the fairness, our algorithm encourages splits that mitigate discrimination. Importantly, in penalizing unfair splits, we account for the uncertainty in the fairness metric by utilizing its confidence interval instead of relying on its point estimate. Experimental results on benchmark and synthetic datasets illustrate that our method effectively reduces discriminatory predictions compared to traditional classification trees, without significant loss in overall accuracy.

The paper presents two nice ways for improving the usual gradient boosting algorithm where weak classifiers are decision trees. It is a paper oriented towards efficient (less costful) implementation of the usual algorithm in order to speed up the learning of decision trees by taking into account previous computations and sparse data. The approaches are interesting and smart. A risk bound is given for one of the improvements (GOSS), which seems sound but still quite loose: according to the experiments, a tighter bound could be obtained, getting rid of the "max" sizes of considered sets. No garantee is given for the second improvement (EFB) although is seems to be quite efficient in practice.

The article tackles the problem of variable importance in regression trees. The strategy is to select the variables based on the impurity reduction they induce on label Y. The main feature of this strategy is that the impurity reduction measure is based on the ordering of Y according to the ranking of the X variable under consideration, therefore it measures the relationship between Y and any variable in a more robust way than simple correlation would. The authors prove that this strategy is consistent (i.e. the true explanatory variables are selected) in a range of settings. This is then illustrated on a simulated example where the results displayed are somewhat the ones one could have expected: the proposed procedure is able to account for monotone but non linear relationships between X and Y so it yields better results than simple correlations.

The authors of this paper present a new decision tree algorithm for the interval regression problem. Leaves are partitioned using a margin based hinge loss similar to the L1-regularized hinge loss in Rigaill et al, Proc ICML 2013. However, the regression tree algorithm presented in this work is not limited to modeling linear patterns as the L1-regularized linear models in Rigaill et al. For training the non linear tree model, a sequence of convex optimization subproblems are optimally solved in log-linear time by Dynamic Programming (DP). The new maximum margin interval tree (MMIT) algorithm is compared with state-of-the-art margin-based and non-margin-based methods in several real and simulated datasets.

Their method requires an initial decision tree. The topology of this tree will be fixed, and only the decision rules at each node will be adjusted. The idea behind the proposed adjustment is based on the observation that, fixing all of the parameters of all the nodes except the parameters of node i, the likelihood function for the whole tree reduces to the likelihood function of a simple K-classes classifier. This simple classifier can be trained efficiently (using existing techniques) and doing so will always guarantee that the overall loss will decrease when compared to the loss for the initial decision tree.

Decision trees are powerful for predictive modeling but often suffer from high variance when modeling continuous relationships. While algorithms like Multivariate Adaptive Regression Splines (MARS) excel at capturing such continuous relationships, they perform poorly when modeling discontinuities. To address the limitations of both approaches, we introduce Spline-based Multivariate Adaptive Regression Trees (SMART), which uses a decision tree to identify subsets of data with distinct continuous relationships and then leverages MARS to fit these relationships independently. Unlike other methods that rely on the tree structure to model interaction and higher-order terms, SMART leverages MARS's native ability to handle these terms, allowing the tree to focus solely on identifying discontinuities in the relationship. We test SMART on various datasets, demonstrating its improvement over state-of-the-art methods in such cases. Additionally, we provide an open-source implementation of our method to be used by practitioners.

Adversarial attacks are major threats to the deployment of machine learning (ML) models in many applications. Testing ML models against such attacks is becoming an essential step for evaluating and improving ML models. In this paper, we report the design and development of an interactive system for aiding the workflow of Testing Against Adversarial Attacks (TA3). In particular, with TA3, human-in-the-loop (HITL) enables human-steered attack simulation and visualization-assisted attack impact evaluation. While the current version of TA3 focuses on testing decision tree models against adversarial attacks based on the One Pixel Attack Method, it demonstrates the importance of HITL in ML testing and the potential application of HITL to the ML testing workflows for other types of ML models and other types of adversarial attacks.