Author: Charlotte Turner

Authors: Felipe Yáñez、Xiaoliang Luo、Omar Valerio Minero、Bradley C. Love Paper: https://arxiv.org/abs/2408.08083 Confidence-Weighted Integration of Human and Machine Judgments for Superior Decision-Making Introduction In today’s information-rich environments, the ability to process vast amounts of data is increasingly challenging for humans. Conversely, machine systems, particularly large language models (LLMs), can leverage extensive information resources and often achieve superhuman performance levels. This raises concerns about whether machines will replace human judgment in critical areas. One potential solution is forming human-machine teams where judgments from both humans and machines are integrated. This paper evaluates the potential of such teams in a knowledge-intensive task where LLMs…

Authors: Homayoun Honari、Amir Mehdi Soufi Enayati、Mehran Ghafarian Tamizi、Homayoun Najjaran Paper: https://arxiv.org/abs/2408.07962 Introduction Reinforcement Learning (RL) has shown significant success in various domains such as energy systems, video games, and robotics. However, deploying RL in real-world scenarios is challenging due to the extensive trial-and-error nature of the learning process, which can lead to unsafe states and potential damage to the system. Safe Reinforcement Learning (Safe RL) aims to address this issue by optimizing policies that comply with predefined constraints, typically using the Lagrangian method. However, the real-world deployment of Lagrangian-based safe RL is hindered by the need for precise threshold tuning,…

Authors: Musa Taib、Jiajun Wu、Steve Drew、Geoffrey G. Messier Paper: https://arxiv.org/abs/2408.07845 Introduction The Housing and Homelessness System of Care (HHSC) aims to connect individuals experiencing homelessness with supportive housing. This system comprises various agencies, each with different information technology platforms, leading to isolated data silos. Larger agencies can train and test artificial intelligence (AI) tools due to their extensive data, but smaller agencies often lack this capability. This paper introduces a Federated Learning (FL) approach to enable all agencies to collaboratively train a predictive model without sharing sensitive data, thereby ensuring equitable access to quality AI tools while preserving privacy. Background Emergency…

Authors: Antonio Almudévar、Alfonso Ortega、Luis Vicente、Antonio Miguel、Eduardo Lleida Paper: https://arxiv.org/abs/2408.07016 Introduction Representation learning aims to discover and extract factors of variation from data. A representation is considered disentangled if it separates these factors in a way that is understandable to humans. Traditional definitions and metrics for disentanglement assume that factors of variation are independent, which is often not the case in real-world scenarios. This paper proposes a new definition of disentanglement based on information theory, valid even when factors of variation are not independent. The authors also relate this definition to the Information Bottleneck Method and propose a method to measure…