2 citations · 2 across the 8 of their papers we have counts for
8 papers
Anytime-Valid Distribution Shift Detection via Predictive Rank Martingales
Qi Kuang, Yin Xia
Many sequential distribution shift detectors update a growing reference set with incoming observations. After a change, this update contaminates the reference set with post-change…
Gaussian Differentially Private -values: Construction, Threshold Calibration, and Multiple Testing
Qi Kuang, Bowen Gang, Yin Xia
This paper develops a framework for differentially private -values under Gaussian differential privacy (-GDP). We characterize the canonical noise mechanism, establishing tha…
Distributional Off-Policy Evaluation with Deep Quantile Process Regression
Qi Kuang, Chao Wang, Yuling Jiao +1
This paper investigates the off-policy evaluation (OPE) problem from a distributional perspective. Rather than focusing solely on the expectation of the total return, as in most ex…
SCORE: A Unified Framework for Overshoot Refund in Online FDR Control
Qi Kuang, Bowen Gang, Yin Xia
We propose a unified framework to enhance the power of online multiple hypothesis testing procedures based on -values. While -value-based methods offer robust online False Di…
Active Hypothesis Testing under Computational Budgets
Qi Kuang, Bowen Gang, Yin Xia
In large-scale hypothesis testing, computing exact -values or -values is often resource-intensive, creating a need for budget-aware inferential methods. We propose a general…
Data-light Uncertainty Set Merging with Admissibility
Shenghao Qin, Jianliang He, Qi Kuang +2
This article introduces a Synthetics, Aggregation, and Test inversion (SAT) approach for merging diverse and potentially dependent uncertainty sets into a single unified set. The p…
Variance Control for Distributional Reinforcement Learning
Qi Kuang, Zhoufan Zhu, Liwen Zhang +1
Although distributional reinforcement learning (DRL) has been widely examined in the past few years, very few studies investigate the validity of the obtained Q-function estimator…
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning
Fan Zhou, Zhoufan Zhu, Qi Kuang +1
Although distributional reinforcement learning (DRL) has been widely examined in the past few years, there are two open questions people are still trying to address. One is how to…