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Schedule, Notes/Handouts & Videos

[Virtual] Hot Topics: Foundations of Stable, Generalizable and Transferable Statistical Learning March 07, 2022 - March 10, 2022

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Mar 07, 2022
Monday
07:55 AM - 08:00 AM
  Welcome
08:00 AM - 08:25 AM
  Datamodels: Predicting Predictions with Training Data
Aleksander Madry (Massachusetts Institute of Technology)
08:30 AM - 08:55 AM
  Domain Adaptation Under Structural Causal Models
Yuansi Chen (Duke University )
09:00 AM - 09:25 AM
  Assessing Replicability Via Multi-lab Collaborations
Blake McShane (Northwestern University)
09:30 AM - 10:00 AM
  Lunch / Dinner Break
10:00 AM - 10:25 AM
  Elements of External Validity: Framework, Design, and Analysis
Erin Hartman (University of California, Berkeley)
10:30 AM - 10:55 AM
  Evaluating Replicability: Considerations for Analyses and Implications for Design
Jacob Schauer (Northwestern University)
11:00 AM - 11:15 AM
  Break
11:15 AM - 12:15 PM
  Discussion
Mar 08, 2022
Tuesday
08:00 AM - 08:25 AM
  Disentangling Confounding and Nonsense Associations Due to Dependence
Betsy Ogburn (Johns Hopkins University)
08:30 AM - 08:55 AM
  Interpretable Sensitivity Analysis for the Baron–Kenny Approach to Mediation with Unmeasured Confounding
Peng Ding (University of California, Berkeley)
09:00 AM - 09:25 AM
  Distribution Generalization in Underidentified Causal Models
Jonas Peters (University of Copenhagen)
09:30 AM - 10:00 AM
  Lunch / Dinner Break
10:00 AM - 10:25 AM
  An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?
Tamara Broderick (Massachusetts Institute of Technology)
10:30 AM - 10:55 AM
  Near-Optimal Compression in Near-Linear Time
Raaz Dwivedi (Harvard University)
11:00 AM - 11:15 AM
  Break
11:15 AM - 12:15 PM
  Discussion
Mar 09, 2022
Wednesday
08:00 AM - 08:25 AM
  A Precise High-Dimensional Asymptotic Theory for AdaBoost
Pragya Sur (Harvard University)
08:30 AM - 08:55 AM
  Prospects and Perils of Interpolating Models
Fanny Yang (ETH Zurich)
09:00 AM - 09:25 AM
  Distributionally Robust Bayesian Nonparametric Regression
Jose Blanchet (Stanford University)
09:30 AM - 10:00 AM
  Lunch / Dinner Break
10:00 AM - 10:25 AM
  Calibrated Inference: Statistical Inference that Accounts for Both Sampling Uncertainty and Distributional Uncertainty
Dominik Rothenhaeusler (Stanford University)
10:30 AM - 10:55 AM
  Assessing External Validity Over Worst-Case Subpopulations
Hongseok Namkoong (Columbia University)
11:00 AM - 11:15 AM
  Break
11:15 AM - 12:15 PM
  Discussion
Mar 10, 2022
Thursday
08:00 AM - 08:25 AM
  Veridical Network Embedding
Tian Zheng (Columbia University)
08:30 AM - 08:55 AM
  Bayesian Nonparametric Models for Treatment Effect Heterogeneity: Model Parameterization, Prior Choice, and Posterior Summarization
Jared Murray (University of Texas, Austin)
09:00 AM - 09:25 AM
  Sim2Real Transfer in Robotics: Thoughts on Model Pruning and Robust Visual Transfer
Bradly Stadie (Toyota technological Institute at Chicago)
09:30 AM - 10:00 AM
  Lunch / Dinner Break
10:00 AM - 10:25 AM
  Predicting Out-of-Distribution Error with the Projection Norm
Jacob Steinhardt (UC Berkeley)
10:30 AM - 10:55 AM
  Structured Adaptation & Deep Learning: When Prediction Yields Adaptation
Zachary Lipton (Carnegie Mellon University)
11:00 AM - 11:15 AM
  Break
11:15 AM - 12:15 PM
  Discussion