To avoid failures on out-of-distribution data, recent works have sought to extract features that
have an invariant or stable relationship with the label across domains, discarding" spurious" …

Objective We developed an artificial intelligence (AI)‐based headache diagnosis model
using a large questionnaire database from a headache‐specializing clinic. Background …

Calibrating deep neural models plays an important role in building reliable, robust AI
systems in safety-critical applications. Recent work has shown that modern neural networks …

Traditional fully-deterministic algorithms, which rely on physical equations and mathematical
models, are the backbone of many scientific disciplines for decades. These algorithms are …

Automated machine learning (AutoML) is a young research area aiming at making high-
performance machine learning techniques accessible to a broad set of users. This is …

Abstract Heating, Ventilation, and Air Conditioning (HVAC) systems in large-scale buildings
often struggle to ensure satisfactory thermal comfort for diverse occupants while minimizing …

Background The healthcare sector demands a higher degree of responsibility,
trustworthiness, and accountability when implementing Artificial Intelligence (AI) systems …

A key requirement in developing Generative Language Models (GLMs) is to have their
values aligned with human values. Preference-based alignment is a widely used paradigm …

There has been an explosion of data collected about sports. Because such data is extremely
rich and complex, machine learning is increasingly being used to extract actionable insights …

Context. The study of active galactic nuclei (AGNs) is fundamental to discern the formation
and growth of supermassive black holes (SMBHs) and their connection with star formation …