The recent developments in the research area indicate a strong focus on addressing challenges related to imbalanced data, causal inference, and semi-supervised learning. A significant trend is the exploration of innovative sampling techniques and model calibration methods to handle class imbalance, which is critical for improving the performance of classification algorithms in real-world applications. Additionally, there is a growing interest in leveraging probabilistic principles and domain-specific insights to enhance machine learning models, particularly in regression and classification tasks. The integration of network information and disentangled representation learning for treatment effect estimation from observational data is also gaining traction, offering new avenues for causal inference. Furthermore, advancements in semi-supervised learning are being driven by the need to optimize the use of both labeled and unlabeled data, with a particular emphasis on risk control and prediction-powered inference. These developments collectively point towards a more nuanced and sophisticated approach to machine learning, where theoretical rigor and practical applicability are balanced to address complex, real-world problems.