Abstract: Causal inference and root cause analysis play a crucial role in network performance evaluation and optimization by identifying critical parameters and explaining how the configuration ...

Abstract: Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in real-world visual applications. To address this issue, domain generalization methods ...

What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...

The reduction of health inequalities has been a priority of researchers, decision-makers and practitioners for many years. Advances in causal mediation analysis offer great promise for identifying ...

ABSTRACT: Determining the causal effect of special education is a critical topic when making educational policy that focuses on student achievement. However, current special education research is ...

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...

Animals excel at seamlessly integrating information from different senses, a capability critical for navigating complex environments. Despite recent progress in multisensory research, the absence of ...

ABSTRACT: Special education services are designed to provide tailored support for students with diverse learning needs, with the expectation of improving academic achievement. This study examines the ...

Please join the Department of Epidemiology Center for Clinical Trials and Evidence Synthesis (CCTES) and Center for Drug Safety and Effectiveness (CDSE) in welcoming Elizabeth Stuart, PhD, AM, Chair ...

oLLM is a lightweight Python library built on top of Huggingface Transformers and PyTorch and runs large-context Transformers on NVIDIA GPUs by aggressively offloading weights and KV-cache to fast ...