The International Conference on Artificial Intelligence and Statistics (AISTATS) 2021 is being hosted virtually from April 13th - April 15th. We’re excited to share all the work from SAIL that’s being presented, and you’ll find links to papers, videos and blogs below. Feel free to reach out to the contact authors directly to learn more about the work that’s happening at Stanford!

List of Accepted Papers

Active Online Learning with Hidden Shifting Domains

Authors: Yining Chen, Haipeng Luo, Tengyu Ma, Chicheng Zhang
Contact: cynnjjs@stanford.edu
Links: Paper
Keywords: online learning, active learning, domain adaptation


A Constrained Risk Inequality for General Losses

Authors: Feng Ruan
Contact: fengruan@stanford.edu
Keywords: constrained risk inequality; super-efficiency


Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation

Authors: Mayee F. Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, Christopher Ré
Contact: mfchen@stanford.edu
Links: Paper
Keywords: latent variable graphical model, method-of-moments, semi-supervised learning, model misspecification


Efficient computation and analysis of distributional Shapley values

Authors: Yongchan Kwon, Manuel A. Rivas, James Zou
Contact: yckwon@stanford.edu
Links: Paper | Website
Keywords: data valuation, distributional shapley value


Improving Adversarial Robustness via Unlabeled Out-of-Domain Data

Authors: Zhun Deng, Linjun Zhang, Amirata Ghorbani, James Zou
Contact: jamesz@stanford.edu
Links: Paper
Keywords: adversarial robustness, deep learning, out of domain data


Misspecification in Prediction Problems and Robustness via Improper Learning

Authors: Annie Marsden, John Duchi, Gregory Valiant
Contact: marsden@stanford.edu
Award nominations: Oral Presentation
Links: Paper
Keywords: machine learning, probabilistic forecasting, statistical learning theory


Online Model Selection for Reinforcement Learning with Function Approximation

Authors: Jonathan Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
Contact: jnl@stanford.edu
Links: Paper
Keywords: reinforcement learning, model selection


Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration

Authors: Shengjia Zhao, Stefano Ermon
Contact: sjzhao@stanford.edu
Award nominations: Oral
Links: Paper | Blog Post
Keywords: uncertainty, trustworthiness, reliability


We look forward to seeing you virtually at AISTATS!