The field of machine learning is witnessing significant developments in conformal prediction and ensemble learning. Researchers are exploring new methods to improve the efficiency and accuracy of classification tasks, particularly in large-scale data settings. One notable direction is the development of algorithms that can efficiently learn minimax risk classifiers, which minimize the maximum expected loss. Another area of focus is conformal prediction, where researchers are working to improve the uncertainty quantification of machine learning models. Notably, cost-sensitive conformal training methods and techniques that leverage class similarity are being developed to enhance predictive efficiency. Furthermore, ensemble learning is being investigated through the lens of linear independence among classifier votes, leading to a deeper understanding of the trade-off between ensemble size and accuracy.

Some noteworthy papers in this area include: The paper on Efficient Large-Scale Learning of Minimax Risk Classifiers presents a learning algorithm that enables efficient learning of minimax risk classifiers with large-scale data. The paper on Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds proposes a simple cost-sensitive conformal training algorithm that does not rely on indicator approximation mechanisms. The paper on Enhancing Conformal Prediction via Class Similarity offers a widely applicable tool for boosting any conformal prediction method on any dataset by leveraging class similarity. The paper on Ensemble Performance Through the Lens of Linear Independence of Classifier Votes in Data Streams investigates the relationship between ensemble size and performance through the lens of linear independence among classifier votes.