Episode 121I 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 predictionRyan 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.Reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. The Gradient Podcast on: Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (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) OutroLinks:* 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?” Get full access to The Gradient at thegradientpub.substack.com/subscribe