Extreme events are of great importance since they often represent impactive occurrences. For instance, in terms of climate and weather, extreme events might be major storms, floods, extreme heat or cold waves, and more. However, they are often located at the tail of the data distribution. Consequently, accurately predicting these extreme events is challenging due to their rarity and irregularity. Prior studies have also referred to this as the out-of-distribution (OOD) problem, which occurs when the distribution of the test data is substantially different from that used for training. In this work, we propose two strategies, reweighting and fine-tuning, to tackle the challenge. Reweighting is a strategy used to force machine learning models to focus on extreme events, which is achieved by a weighted loss function that assigns greater penalties to the prediction errors for the extreme samples relative to those on the remainder of the data. Unlike previous intuitive reweighting methods based on simple heuristics of data distribution, we employ meta-learning to dynamically optimize these penalty weights. To further boost the performance on extreme samples, we start from the reweighted models and fine-tune them using only rare extreme samples. Through extensive experiments on multiple data sets, we empirically validate that our meta-learning-based reweighting outperforms existing heuristic ones, and the fine-tuning strategy can further increase the model performance. More importantly, these two strategies are model-agnostic, which can be implemented on any type of neural network for time series forecasting. The open-sourced code is available at \url{https://github.com/JimengShi/ReFine}.
Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting
TUM School of Computation, Information and Technology（慕尼黑技术大学计算、信息与技术学院）；Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Computing, Imperial College London（伦敦帝国学院计算机系）
Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting
National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）；Renmin University of China（中国人民大学）；University of Science and Technology of China（中国科学技术大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
Projected Boosting with Fairness Constraints: Quantifying the Cost of Fair Training Distributions
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA（生物统计学系，范德比尔特大学医学中心）；Department of Informatics, King's College London, WC2R 2LS London, UK（信息学系，伦敦国王学院）
OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad
Xiamen University（厦门大学）；Shenzhen University（深圳大学）；The Australian National University（澳大利亚国立大学）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
MAIS\&NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China（MAIS与NLPR，自动化研究所，中国科学院，北京，中国）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China（先进交叉学科学院，中国科学院大学，北京，中国）
Towards Effective Graph Rationalization via Boosting Environment Diversity
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；School of Computer and Information Technology, Shanxi University（山西大学计算机与信息技术学院）
Exploiting random projections and sparsity with random forests and gradient boosting methods -- Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity
University of Liège（列日大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；Katholieke Universiteit Leuven（鲁汶天主教大学）
Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge
Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）；Halicioğlu Data Science Institute (HDSI), University of California San Diego（加州大学圣迭戈分校哈利乔格鲁数据科学研究所）；Guangdong Institute of Intelligence Science and Technology（广东省智能科学与技术研究院）
RoboGene: Diversity-Driven Agentic Task Generation Framework for VLA Pre-training
Beijing Innovation Center of Humanoid Robotics（北京人形机器人创新中心）；The School of Advanced Manufacturing and Robotics, Peking University（北京大学先进制造与机器人学院）；Beijing Institute of Technology（北京理工大学）；The School of Mechanical Engineering and Automation, Beihang University（北航机械工程与自动化学院）；State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（北京大学多媒体信息处理国家重点实验室）
