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?”

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