University of Toronto（多伦多大学）；University of Wisconsin（威斯康星大学）；IIT Bombay（印度理工学院孟买分校）；Carnegie Mellon University（卡内基梅隆大学）；Stanford University（斯坦福大学）
Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, Colin White
The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications. The climate change AI (CCAI) community works on a diverse, challenging set of problems which often involve physics-constrained ML or heterogeneous spatiotemporal data. It would be desirable to use automated machine learning (AutoML) techniques to automatically find high-performing architectures and hyperparameters for a given dataset. In this work, we benchmark popular AutoML libraries on three high-leverage CCAI applications: climate modeling, wind power forecasting, and catalyst discovery. We find that out-of-the-box AutoML libraries currently fail to meaningfully surpass the performance of human-designed CCAI models. However, we also identify a few key weaknesses, which stem from the fact that most AutoML techniques are tailored to computer vision and NLP applications. For example, while dozens of search spaces have been designed for image and language data, none have been designed for spatiotemporal data. Addressing these key weaknesses can lead to the discovery of novel architectures that yield substantial performance gains across numerous CCAI applications. Therefore, we present a call to action to the AutoML community, since there are a number of concrete, promising directions for future work in the space of AutoML for CCAI. We release our code and a list of resources at https://github.com/climate-change-automl/climate-change-automl.
Position: A Call to Action for a Human-Centered AutoML Paradigm
L3S Research Center（L3S研究中心）；Ludwig-Maximilians-Universität München（慕尼黑大学）；Fraunhofer Institute for Integrated Circuits IIS, Fraunhofer IIS（弗劳恩霍夫集成电路研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Microsoft（微软公司）；Albert-Ludwigs-Universität Freiburg（阿尔伯特-路德维希-弗莱堡大学）
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Amazon Sustainability Science and Innovation（亚马逊可持续性科学与创新部门）
University of Pennsylvania（宾夕法尼亚大学）；Carnegie Mellon University（卡内基梅隆大学）；ETH Zürich（苏黎世联邦理工学院）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Element AI；Mila；Université de Montréal（蒙特利尔大学）；Harvard University（哈佛大学）；Mercator Research Institute on Global Commons and Climate Change（墨卡托全球公域与气候变化研究所）；Technische Universität Berlin（柏林工业大学）；Massachusetts Institute of Technology（麻省理工学院）；École Polytechnique de Montréal（蒙特利尔高等理工学院）；Cornell University（康奈尔大学）；Stanford University（斯坦福大学）；DeepMind；Google AI（谷歌AI）
Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet
Imperial College London（伦敦帝国理工学院）；University of Augsburg（奥格斯堡大学）；audEERING GmbH（audEERING公司）；University Carlos III of Madrid（马德里卡洛斯三世大学）；Medical University of Graz（格拉茨医科大学）
Augmenting The Weather: A Hybrid Counterfactual-SMOTE Algorithm for Improving Crop Growth Prediction When Climate Changes
College of Computer & Cyber Sciences, University of Prince Mugrin（穆格林王子大学计算机与网络科学学院）；School of Computer Science, University College Dublin（都柏林大学学院计算机科学学院）；Insight Centre for Data Analytics, University College Dublin（都柏林大学学院洞察数据分析中心）；VistaMilk SFI Research Centre, University College Dublin（都柏林大学学院VistaMilk SFI研究中心）
Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles
Palo Alto Research Center（帕洛阿尔托研究中心）；University of California, San Diego（加州大学圣迭戈分校）；University of Victoria（维多利亚大学）；Excarta；University of Washington（华盛顿大学）
Europe's Climate Ambition Under Scrutiny: Evidence from Deep Learning Emission Projections
Politecnico di Milano（米兰理工大学）；CMCC Foundation - Euro-Mediterranean Center on Climate Change（CMCC基金会-欧地中海气候变化中心）；RFF-CMCC European Institute on Economics and the Environment（RFF-CMCC欧洲经济与环境研究所）；NOVA School of Science and Technology（NOVA科学技术学院）；NOVA University Lisbon（里斯本NOVA大学）；CENSE—Center for Environmental and Sustainability Research（CENSE环境与可持续发展研究中心）；CHANGE—Global Change and Sustainability Institute（CHANGE全球变化与可持续发展研究所）
Causal Representation Learning in Temporal Data via Single-Parent Decoding
Université de Montréal（蒙特利尔大学）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Samsung - SAIT AI Lab, Montreal（三星SAIT人工智能实验室蒙特利尔分部）；McGill University（麦吉尔大学）；Intel Labs（英特尔实验室）；ServiceNow Research（ServiceNow研究院）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Technische Universität Dresden（德累斯顿工业大学）
