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.

Face Recognition (FR) tasks have made significant progress with the advent of Deep Neural Networks, particularly through margin-based triplet losses that embed facial images into high-dimensional feature spaces. During training, these contrastive losses focus exclusively on identity information as labels. However, we observe a multiscale geometric structure emerging in the embedding space, influenced by interpretable facial (e.g., hair color) and image attributes (e.g., contrast). We propose a geometric approach to describe the dependence or invariance of FR models to these attributes and introduce a physics-inspired alignment metric. We evaluate the proposed metric on controlled, simplified models and widely used FR models fine-tuned with synthetic data for targeted attribute augmentation. Our findings reveal that the models exhibit varying degrees of invariance across different attributes, providing insight into their strengths and weaknesses and enabling deeper interpretability. Code available here: https://github.com/mantonios107/attrs-fr-embs.

We investigate the role of the initial screening order (ISO) in candidate screening. The ISO refers to the order in which the screener searches the candidate pool when selecting k candidates. Today, it is common for the ISO to be the product of an information access system, such as an online platform or a database query. The ISO has been largely overlooked in the literature, despite its impact on the optimality and fairness of the selected k candidates, especially under a human screener. We define two problem formulations describing the search behavior of the screener given an ISO: the best-k, where it selects the top k candidates; and the good-k, where it selects the first good-enough k candidates. To study the impact of the ISO, we introduce a human-like screener and compare it to its algorithmic counterpart, where the human-like screener is conceived to be inconsistent over time. Our analysis, in particular, shows that the ISO, under a human-like screener solving for the good-k problem, hinders individual fairness despite meeting group fairness, and hampers the optimality of the selected k candidates. This is due to position bias, where a candidate's evaluation is affected by its position within the ISO. We report extensive simulated experiments exploring the parameters of the best-k and good-k problems for both screeners. Our simulation framework is flexible enough to account for multiple candidate screening tasks, being an alternative to running real-world procedures.