Stable Classifier

Stable classifier research aims to develop machine learning models robust to data perturbations and distribution shifts, ensuring reliable predictions even with noisy or evolving data. Current efforts focus on techniques like bagging, inflated argmax functions, and invariant feature selection to improve stability, often employing logistic regression, neural networks, or mean-field models. This work is crucial for building trustworthy AI systems in various applications, from improving the efficiency of particle accelerators to mitigating the impact of adversarial attacks on image recognition. The ultimate goal is to create classifiers that are not only accurate but also consistently reliable in diverse and unpredictable environments.