Robust Learning

Machine learning algorithms have many applications. Can we theoretically prove they are consistent and helpful? We focus on two paradigms: algorithmic stability and algorithms with predictions. Stable algorithms, which can tolerate changes in their inputs, can inherit many desirable properties such as generalization, differential privacy, and replicability. Algorithms with predictions use their predictions to circumvent worst-case bounds when the predictions are good, and mitigate the effect of bad predictions