arXiv:2310.06746v3 Announce Type: replace Abstract: Interpretability plays a crucial role in the application of statistical learning to estimate heterogeneous treatment effects (HTE) in complex diseases. In this study, we leverage a rule-based workflow, namely causal rule learning (CRL), to estimate…
arXiv:2511.16849v1 Announce Type: new Abstract: Artificial neural networks (ANNs) are increasingly powerful models of brain computation, yet it remains unclear whether improving their task performance also makes their internal representations more similar to brain signals. To address this question in…
arXiv:2503.09114v2 Announce Type: replace Abstract: The rapid rise of Language Models (LMs) has expanded the capabilities of natural language processing, powering applications from text generation to complex decision-making. While state-of-the-art LMs often boast hundreds of billions of parameters and are…
arXiv:2511.16856v1 Announce Type: new Abstract: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that, in addition to directly impairing functional mobility, is frequently associated with vocal impairments such as hypophonia and dysarthria, which typically manifest in the early stages. The…
arXiv:2506.08274v5 Announce Type: replace Abstract: This research addresses the critical lack of comprehensive studies on feature scaling by systematically evaluating 12 scaling techniques – including several less common transformations – across 14 different Machine Learning algorithms and 16 datasets for…
arXiv:2511.16871v1 Announce Type: new Abstract: Graph Neural Networks rely on local message passing, which limits their ability to model long-range dependencies in graphs. Existing approaches extend this range through continuous-time dynamics or dense self-attention, but both suffer from high computational…
arXiv:2509.05826v2 Announce Type: replace Abstract: Conformal prediction is a model-agnostic approach to generating prediction sets that cover the true class with a high probability. Although its prediction set size is expected to capture aleatoric uncertainty, there is a lack of…
arXiv:2511.16883v1 Announce Type: new Abstract: The growing number of Large Language Models (LLMs) with diverse capabilities and response styles provides users with a wider range of choices, which presents challenges in selecting appropriate LLMs, as user preferences vary in terms…
arXiv:2511.00423v2 Announce Type: replace Abstract: Online planning has proven effective in reinforcement learning (RL) for improving sample efficiency and final performance. However, using planning for environment interaction inevitably introduces a divergence between the collected data and the policy’s actual behaviors,…
arXiv:2511.16905v1 Announce Type: new Abstract: Early detection of rising talents is of paramount importance in the field of advertising. In this paper, we define a concept of talent breakout and propose a method to detect Japanese talents before their rise…
