13 citations · 32 across the 13 of their papers we have counts for
13 papers
Bringing Closure to False Discovery Rate Control: A General Principle for Multiple Testing
Ziyu Xu, Aldo Solari, Lasse Fischer +3
We present a novel necessary and sufficient principle for multiple testing methods controlling an expected loss. This principle asserts that every such multiple testing method is a…
Bringing closure to FDR control: beating the e-Benjamini-Hochberg procedure
Ziyu Xu, Lasse Fischer, Aaditya Ramdas
False discovery rate (FDR) has been a key metric for error control in multiple hypothesis testing, and many methods have developed for FDR control across a diverse cross-section of…
Active multiple testing with proxy p-values and e-values
Ziyu Xu, Catherine Wang, Larry Wasserman +2
Researchers often lack the resources to test every hypothesis of interest directly or compute test statistics comprehensively, but often possess auxiliary data from which we can co…
An online generalization of the (e-)Benjamini-Hochberg procedure
Lasse Fischer, Ziyu Xu, Aaditya Ramdas
In online multiple testing, the hypotheses arrive one by one, and at each time we must immediately reject or accept the current hypothesis solely based on the data and hypotheses o…
Online multiple testing with e-values
Ziyu Xu, Aaditya Ramdas
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. The scientist wis…
Risk-limiting Financial Audits via Weighted Sampling without Replacement
Shubhanshu Shekhar, Ziyu Xu, Zachary C. Lipton +2
We introduce the notion of a risk-limiting financial auditing (RLFA): given transactions, the goal is to estimate the total misstated monetary fraction~() to a given accur…
More powerful multiple testing under dependence via randomization
Ziyu Xu, Aaditya Ramdas
We develop a technique to improve the power of any e-value by a simple randomization involving one independent uniform random variable. Using this framework, we show that two proce…
Memory Bounds for the Experts Problem
Vaidehi Srinivas, David P. Woodruff, Ziyu Xu +1
Online learning with expert advice is a fundamental problem of sequential prediction. In this problem, the algorithm has access to a set of "experts" who make predictions on ea…
Post-selection inference for e-value based confidence intervals
Ziyu Xu, Ruodu Wang, Aaditya Ramdas
Suppose that one can construct a valid -confidence interval (CI) for each of parameters of potential interest. If a data analyst uses an arbitrary data-dependent criteri…
A unified framework for bandit multiple testing
Ziyu Xu, Ruodu Wang, Aaditya Ramdas
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 identi…
Dynamic Algorithms for Online Multiple Testing
Ziyu Xu, Aaditya Ramdas
We derive new algorithms for online multiple testing that provably control false discovery exceedance (FDX) while achieving orders of magnitude more power than previous methods. Th…
Class-Weighted Classification: Trade-offs and Robust Approaches
Ziyu Xu, Chen Dan, Justin Khim +1
We address imbalanced classification, the problem in which a label may have low marginal probability relative to other labels, by weighting losses according to the correct class. F…
Multiclass Classification via Class-Weighted Nearest Neighbors
Justin Khim, Ziyu Xu, Shashank Singh
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…