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!