The Stanford Statistical Machine Learning Group at Stanford is a unique blend of faculty, students, and post-docs spanning AI, systems, theory, and statistics. Our work spans the spectrum from answering deep, foundational questions in the theory of machine learning to building practical large-scale machine learning algorithms which are widely used in industry. Topics include reliable machine learning, large-scale optimization, interactive learning, unsupervised and semi-supervised learning, reinforcement learning, deep learning, and statistical learning theory.
- Yuanzhi Li, Tengyu Ma, Hongyang Zhang. Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations. Best paper award at COLT 2018.
- Hongseok Namkoong, John Duchi. Variance-based Regularization with Convex Objectives. Best paper award at NIPS 2017.
- Shayan Doroudi, Philip S. Thomas, Emma Brunskill. Importance Sampling for Fair Policy Selection. Best paper award at UAI 2017.
- Pang Wei Koh, Percy Liang. Understanding Black-box Predictions via Influence Functions. Best paper award at ICML 2017.
- Yuchen Zhang, Percy Liang, Moses Charikar. A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics. Best paper award at COLT 2017.
- Russell Stewart, Stefano Ermon. Label-Free Supervision of Neural Networks with Physics and Domain Knowledge. Outstanding paper award at AAAI 2017.