Predicting the success of startup companies is of great importance for both startup companies and investors. It is difficult due to the lack of available data and appropriate general methods. With data platforms like Crunchbase aggregating the information of startup companies, it is possible to predict with machine learning algorithms. Existing research suffers from the data sparsity problem as most early-stage startup companies do not have much data available to the public. We try to leverage the recent algorithms to solve this problem. We investigate several machine learning algorithms with a large dataset from Crunchbase. The results suggest that LightGBM and XGBoost perform best and achieve 53.03% and 52.96% F1 scores. We interpret the predictions from the perspective of feature contribution. We construct portfolios based on the models and achieve high success rates. These findings have substantial implications on how machine learning methods can help startup companies and investors.
Rule Forest: Interpretable Ensembles of LLM-Generated Questions for Rare-Outcome Prediction
University of Oxford, United Kingdom Aarhus University, Aarhus, Denmark Centre de Recerca Matem\`atica, Barcelona, Spain Oliver Wyman, New York, United States Vela Research, San Francisco, United States
CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction
Department of Computer Science University of Kentucky（计算机科学系 俄亥俄大学）；Department of Computer Science, Institute for Biomedical Informatics University of Kentucky（计算机科学系 生物医学信息学研究所 俄亥俄大学）
Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach
School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia（公共卫生与预防医学学院，墨尔本大学）；School of Civil and Environmental Engineering, Research Centre for Integrated Transport Innovation (rCITI)（土木与环境工程学院，综合交通创新研究中心）；School of Information and Communication Technology, Griffith University, Brisbane, Australia（信息与通信技术学院，格里菲斯大学）
Utilizing Large Language Models for Machine Learning Explainability
Centre for Research \& Technology, Hellas School of Architecture Technology；Engineering, University of Brighton, BN2 4GJ, United Kingdom University of Nicosia, 2417 Cyprus
Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks
Institute for Intelligent Systems, University of Memphis（孟菲斯大学智能系统研究所）；Faculty of Education, The University of Hong Kong（香港大学教育学院）；Human-Computer Interaction Institute, Carnegie Mellon University（卡内基梅隆大学人机交互研究所）；Centre for Learning Analytics, Faculty of Information Technology, Monash University（莫纳什大学信息技术学院学习分析中心）；Educational Testing Service（教育考试服务中心）；Department of Psychology and Counseling, Arkansas State University（阿肯色州立大学心理学与咨询系）；Department of Applied Social Sciences, Hong Kong Polytechnic University（香港理工大学应用社会科学系）
