Adaptive data analysis is the increasingly common practice by which insights gathered from data are used to inform further analysis of the same data sets. This is common practice both in machine learning, and in scientific research, in which data-sets are shared and re-used across multiple studies. Unfortunately, most of the statistical inference theory used in empirical sciences to control false discovery rates, and in machine learning to avoid overfitting, assumes a fixed class of hypotheses to test, or family of functions to optimize over, selected independently of the data. If the set of analyses run is itself a function of the data, much of this theory becomes invalid, and indeed, has been blamed as one of the causes of the crisis of reproducibility in empirical science.

Recently, there have been several exciting proposals for how to avoid overfitting and guarantee statistical validity even in general adaptive data analysis settings. The problem is important, and ripe for further advances. The goal of this workshop is to bring together members of different communities (from machine learning, statistics, and theoretical computer science) interested in solving this problem, to share recent results, to discuss promising directions for future research, and to foster collaborations. The workshop will consist several sessions of invited talks, with panel discussions consisting of the speakers following each session.