Gini Score under Ties and Case Weights

Contemporary machine learning models, including large language models, struggle in non-stationary environments due to rigid architectures. This work introduces dynamic nested hierarchies, allowing models to autonomously adjust optimization levels and structures during training or inference. Inspired by neuroplasticity, this approach aims to facilitate lifelong learning by enabling self-evolution without predefined constraints.

This study introduces a novel model-agnostic attribution method for time series classification, focusing on assessing the causal effects of predefined segments on classification outcomes. It contrasts causal attributions with traditional associational methods, utilizing state-of-the-art diffusion models to estimate counterfactual outcomes. The findings provide insights into various time series classification tasks, enhancing the understanding of machine learning decision-making processes.

The article discusses the challenges faced in machine learning as applications become more complex and require more than just accuracy. It highlights the prevalent method of aggregating penalties for requirement violations into training objectives, which necessitates careful tuning of hyperparameters. This tuning process can become ineffective with a moderate number of requirements, especially when dealing with equality constraints related to fairness. The work presented derives a generalization theory for equality-constrained statistical learning problems.

The study presents a new classifier utilizing byte n-grams for malware detection, which meets critical requirements for size, speed, and latency. The research indicates that 6-8 grams yield optimal accuracy in production settings. However, the challenge of regularly updating models due to the high cost of identifying the most frequent n-grams in large datasets has been a barrier. The introduction of the Zipf-Gramming algorithm significantly enhances the speed of extracting top-k n-grams, resulting in a 30% improvement in AUC for detecting new malware.

This technical note discusses the derivative of the truncated singular value and eigenvalue decomposition, which is crucial for applications in machine learning and computational physics. It emphasizes the need for stable and efficient linear algebra gradient computations, particularly in the context of automatic differentiation techniques. The note builds on previous work, providing a detailed explanation of how to derive the relevant terms while focusing on expressing the derivative concerning the truncated part of the decomposition.

Mobile jammers present a significant threat to 5G networks, especially in military settings. An innovative anti-jamming framework has been proposed, utilizing Multiple Signal Classification (MUSIC) for precise Direction-of-Arrival (DoA) estimation and Minimum Variance Distortionless Response (MVDR) beamforming for adaptive interference suppression. Machine learning enhances DoA prediction for mobile jammers. Simulations indicate an average Signal-to-Noise Ratio (SNR) improvement of 9.58 dB and a DoA estimation accuracy of up to 99.8%, showcasing the framework's effectiveness in dynamic environ…

A new machine learning-based multimodal framework has been developed for wearable sensor-based archery action recognition and stress estimation. This innovative system utilizes a wrist-worn device equipped with an accelerometer and photoplethysmography (PPG) sensor to collect synchronized motion and physiological data during archery sessions. The framework achieves high accuracy in motion recognition and stress estimation, marking a significant advancement in the analysis of athletes' performance in precision sports.

The article discusses the application of machine learning in representing subgrid-scale (SGS) dynamics, particularly in the context of quasi-geostrophic turbulence within planetary interiors. It highlights the effectiveness of online end-to-end learning methods, which involve the dynamical solver in the training process, showing superior performance compared to traditional physics-based models. The study focuses on two-dimensional turbulent flow in axisymmetric bounded domains, exploring various geometries and rotation rates.