v244: Proceedings of UAI 2024

2026-06-10 · Source: Proceedings of Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

The Fortieth Conference on Uncertainty in Artificial Intelligence (UAI 2024) proceedings, Volume 244, presents a comprehensive collection of research from July 15-19, 2024, in Barcelona, Spain. Edited by Negar Kiyavash and Joris M. Mooij, these papers delve into critical aspects of uncertainty management across diverse AI applications. Key research areas include advancements in causal inference, robust and federated learning, Bayesian deep learning, and novel methods for quantifying uncertainty in large language models and neural networks. The contributions offer theoretical insights, algorithmic innovations, and practical solutions to enhance the reliability, interpretability, and fairness of artificial intelligence systems. Many papers also provide associated software, facilitating further research and application.

Key takeaway

Volume 244 of the Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence (UAI 2024) presents over 200 research papers addressing fundamental challenges in AI reliability. Key contributions span advanced uncertainty quantification techniques for LLMs and diffusion models (e.g., CSS, DECU), novel causal inference methods (e.g., Adjustment Identification Distance, Causal Spaces), and robust learning algorithms for federated and adversarial settings. This resource provides AI/ML researchers and practitioners with state-of-the-art theoretical frameworks and practical software implementations to enhance the trustworthiness, interpretability, and performance of intelligent systems.

Topics

• Uncertainty Quantification
• Causal Inference
• Reinforcement Learning
• Bayesian Deep Learning
• Federated Learning

Code references

• stevenan5/balsubramani-freund-uai-2024
• rafaelanderka/iter-inla
• AoShuang92/css_uq_llms
• imadaouali/unified-pessimism-opl
• CEA-LIST/LaREx

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.