Search Results for author:
Found 8 papers, 4 papers with code
Online multiple testing with e-values
A scientist tests a continuous stream of hypotheses over time in the course of her investigation -- she does not test a predetermined, fixed number of hypotheses.
Risk-limiting Financial Audits via Weighted Sampling without Replacement
Next, we develop methods to improve the quality of CSs by incorporating side information about the unknown values associated with each item.
Memory Bounds for the Experts Problem
We initiate the study of the learning with expert advice problem in the streaming setting, and show lower and upper bounds.
A unified framework for bandit multiple testing
In bandit multiple hypothesis testing, each arm corresponds to a different null hypothesis that we wish to test, and the goal is to design adaptive algorithms that correctly identify large set of interesting arms (true discoveries), while only mistakenly identifying a few uninteresting ones (false discoveries).
Dynamic Algorithms for Online Multiple Testing
This statistical advance is enabled by the development of new algorithmic ideas: earlier algorithms are more "static" while our new ones allow for the dynamical adjustment of testing levels based on the amount of wealth the algorithm has accumulated.
Class-Weighted Classification: Trade-offs and Robust Approaches
We define a robust risk that minimizes risk over a set of weightings and show excess risk bounds for this problem.
Multiclass Classification via Class-Weighted Nearest Neighbors
We study statistical properties of the k-nearest neighbors algorithm for multiclass classification, with a focus on settings where the number of classes may be large and/or classes may be highly imbalanced.
