arXiv:2601.10084v1 Announce Type: new Abstract: Machine learning classification systems are susceptible to poor performance when trained with incorrect ground truth labels, even when data is well-curated by expert annotators. As machine learning becomes more widespread, it is increasingly imperative to…
arXiv:2503.03283v2 Announce Type: replace-cross Abstract: Drawing parallels with the way biological networks are studied, we adapt the treatment–control paradigm to explainable artificial intelligence research and enrich it through multi-parametric input alterations. In this study, we propose a framework for investigating…
arXiv:2601.10089v1 Announce Type: new Abstract: The meta-analysis’s utility is dependent on previous studies having accurately captured the variables of interest, but in medical studies, a key decision variable that impacts a physician’s decisions was not captured. This results in an…
arXiv:2505.21441v4 Announce Type: replace-cross Abstract: We propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a low-dimensional embedding of the model that optimally represents relationships…
arXiv:2601.10092v1 Announce Type: new Abstract: Multimodal clinical prediction is widely used to integrate heterogeneous data such as Electronic Health Records (EHR) and biosignals. However, existing methods tend to rely on static modality integration schemes and simple fusion strategies. As a…
Delia Wendel’s new book illuminates a painful and painstaking effort by citizens to bear witness to atrocities.
arXiv:2410.03055v3 Announce Type: replace Abstract: Large Language Models (LLMs) are rapidly becoming commodity components of larger software systems. This poses natural security and privacy problems: poisoned data retrieved from one component can change the model’s behavior and compromise the entire…
arXiv:2601.04377v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as an important means of enhancing the performance of large language models (LLMs) in knowledge-intensive tasks. However, most existing RAG strategies treat retrieved passages in a flat and unstructured way,…
arXiv:2407.18454v3 Announce Type: replace-cross Abstract: Language Models (LMs) have demonstrated exceptional performance across various Natural Language Processing (NLP) tasks. Despite these advancements, LMs can inherit and amplify societal biases related to sensitive attributes such as gender and race, limiting their…
arXiv:2505.08814v2 Announce Type: replace-cross Abstract: Deep neural networks (DNNs) play a crucial role in the field of artificial intelligence, and their security-related testing has been a prominent research focus. By inputting test cases, the behavior of models is examined for…
