Ryan Tibshirani: Statistics, Non Parametric Regression, Conformal Prediction

Episode 121

I spoke with Professor Ryan Tibshirani about:

• Differences between the ML and statistics communities in scholarship, terminology, and other areas.

• Trend filtering

• Why you can’t just use garbage prediction functions when doing conformal prediction

Ryan is a Professor in the Department of Statistics at UC Berkeley. He is also a Principal Investigator in the Delphi group. From 2011-2022, he was a faculty member in Statistics and Machine Learning at Carnegie Mellon University. From 2007-2011, he did his Ph.D. in Statistics at Stanford University.

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Outline:

• (00:00) Intro

• (01:10) Ryan’s background and path into statistics

• (07:00) Cultivating taste as a researcher

• (11:00) Conversations within the statistics community

• (18:30) Use of terms, disagreements over stability and definitions

• (23:05) Nonparametric Regression

  • (23:55) Background on trend filtering

  • (33:48) Analysis and synthesis frameworks in problem formulation

  • (39:45) Neural networks as a specific take on synthesis

  • (40:55) Divided differences, falling factorials, and discrete splines

    • (41:55) Motivations and background

    • (48:07) Divided differences vs. derivatives, approximation and efficiency

• (51:40) Conformal prediction

  • (52:40) Motivations

  • (1:10:20) Probabilistic guarantees in conformal prediction, choice of predictors

  • (1:14:25) Assumptions: i.i.d. and exchangeability — conformal prediction beyond exchangeability

  • (1:25:00) Next directions

• (1:28:12) Epidemic forecasting — COVID-19 impact and trends survey

  • (1:29:10) Survey methodology

  • (1:38:20) Data defect correlation and its limitations for characterizing datasets

• (1:46:14) Outro

Links:

• Ryan’s homepage

• Works read/mentioned

  • Nonparametric Regression

    • Adaptive Piecewise Polynomial Estimation via Trend Filtering (2014) 

    • Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems (2020)

  • Distribution-free Inference

    • Distribution-Free Predictive Inference for Regression (2017)

    • Conformal Prediction Under Covariate Shift (2019)

    • Conformal Prediction Beyond Exchangeability (2023)

  • Delphi and COVID-19 research

    • Flexible Modeling of Epidemics

    • Real-Time Estimation of COVID-19 Infections

    • The US COVID-19 Trends and Impact Survey and Big data, big problems: Responding to “Are we there yet?”