Upcoming Seminar Presentations
All seminars take place Thursdays at 8:30 am PT / 11:30 am ET / 4:30 pm London / 6:30 pm Tel Aviv. Past seminar presentations are posted here.
Thursday, March 11, 2021 [Link to join]
Speaker: Stephen Bates (UC Berkeley)
Title: Distribution-Free, Risk-Controlling Prediction Sets
Abstract: While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making. Deploying learning systems in consequential settings also requires calibrating and communicating the uncertainty of predictions. To convey instance-wise uncertainty for prediction tasks, we show how to generate set-valued predictions from a black-box predictor that control the expected loss on future test points at a user-specified level. Our approach provides explicit finite-sample guarantees for any dataset by using a holdout set to calibrate the size of the prediction sets. This framework enables simple, distribution-free, rigorous error control for many tasks, and we demonstrate it in five large-scale machine learning problems: (1) classification problems where some mistakes are more costly than others; (2) multi-label classification, where each observation has multiple associated labels; (3) classification problems where the labels have a hierarchical structure; (4) image segmentation, where we wish to predict a set of pixels containing an object of interest; and (5) protein structure prediction. Lastly, we discuss extensions to uncertainty quantification for ranking, metric learning and distributionally robust learning.
Discussant: Vladimir Vovk (Royal Holloway, University of London)
Links: [Relevant papers: paper #1]
Thursday, March 18, 2021 [Link to join]
Speaker: Ruodu Wang (University of Waterloo)
Title: Multiple hypothesis testing with e-values and dependence
Abstract: E-values have gained attention as potential alternatives to p-values as measures of uncertainty, significance and evidence. In brief, e-values are realized by random variables with expectation at most one under the null; examples include betting scores, (point null) Bayes factors, likelihood ratios and stopped supermartingales. We design a natural analog of the Benjamini-Hochberg (BH) procedure for false discovery rate (FDR) control that utilizes e-values, called the e-BH procedure, and compare it with the standard procedure for p-values. One of our central results is that, unlike the usual BH procedure, the e-BH procedure controls the FDR at the desired level---with no correction---for any dependence structure between the e-values. We illustrate that the new procedure is convenient in various settings of complicated dependence, structured and post-selection hypotheses, and multi-armed bandit problems. Moreover, the BH procedure is a special case of the e-BH procedure through calibration between p-values and e-values. Overall, the e-BH procedure is a novel, powerful and general tool for multiple testing under dependence, that is complementary to the BH procedure, each being an appropriate choice in different applications.
Discussant: Lihua Lei (Stanford University)
Thursday, April 1, 2021 [Link to join]
Speaker: Jingshen Wang (UC Berkeley)
Title: Sharp Inference on Selected Subgroups in Observational Studies
Abstract: In modern drug development, the broader availability of high-dimensional observational data provides opportunities for scientist to explore subgroup heterogeneity, especially when randomized clinical trials are unavailable due to cost and ethical constraints. However, a common practice that naively searches the subgroup with a high treatment level is often misleading due to the “subgroup selection bias.” More importantly, the nature of high-dimensional observational data has further exacerbated the challenge of accurately estimating the subgroup treatment effects. To resolve these issues, we provide new inferential tools based on resampling to assess the replicability of post-hoc identified subgroups from observational studies. Through careful theoretical justification and extensive simulations, we show that our proposed approach delivers asymptotically sharp confidence intervals and debiased estimates for the selected subgroup treatment effects in the presence of high-dimensional covariates. We further demonstrate the merit of the proposed methods by analyzing the UK Biobank data. The R package “debiased.subgroup" implementing the proposed procedures is available on GitHub.
Thursday, April 15, 2021 [Link to join]
Speaker: Todd Kuffer (Washington University in St. Louis)
The seminars are held on Zoom and last 60 minutes:
45 minutes of presentation
15 minutes of discussion, led by an invited discussant
Moderators collect questions using the Q&A feature during the seminar.
How to join
You can attend by clicking the link to join (there is no need to register in advance).
More instructions for attendees can be found here.
What is selective inference?
Broadly construed, selective inference means searching for interesting patterns in data, usually with inferential guarantees that account for the search process. It encompasses:
Multiple testing: testing many hypotheses at once (and paying disproportionate attention to rejections)
Post-selection inference: examining the data to decide what question to ask, or what model to use, then carrying out one or more appropriate inferences
Adaptive / interactive inference: sequentially asking one question after another of the same data set, where each question is informed by the answers to preceding questions
Cheating: cherry-picking, double dipping, data snooping, data dredging, p-hacking, HARKing, and other low-down dirty rotten tricks; basically any of the above, but done wrong!