11 citations · 30 across the 19 of their papers we have counts for
19 papers
Li, Li, and Dai's Contribution to the Discussion of "Estimating Means of Bounded Random Variables by Betting" by Waudby-Smith and Aaditya Ramdas
Yijia Li, Yuantong Li, Xiaowu Dai
We congratulate Waudby-Smith and Ramdas for their interesting paper \cite{waudbysmith2022estimating} in generating confidence intervals and time-uniform confidence sequences for me…
An ODE Model for Dynamic Matching in Heterogeneous Networks
Xiaowu Dai, Hengzhi He
We study the problem of dynamic matching in heterogeneous networks, where agents are subject to compatibility restrictions and stochastic arrival and departure times. In particular…
A Robust Multi-Item Auction Design with Statistical Learning
Jiale Han, Xiaowu Dai
We propose a novel statistical learning method for multi-item auctions that incorporates credible intervals. Our approach employs nonparametric density estimation to estimate credi…
Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints
Yuantong Li, Guang Cheng, Xiaowu Dai
In this paper, we propose a new recommendation algorithm for addressing the problem of two-sided online matching markets with complementary preferences and quota constraints, where…
Incentive-Aware Recommender Systems in Two-Sided Markets
Xiaowu Dai, Wenlu Xu, Yuan Qi +1
Online platforms in the Internet Economy commonly incorporate recommender systems that recommend products (or "arms") to users (or "agents"). A key challenge in this domain arises…
Nonparametric Estimation via Partial Derivatives
Xiaowu Dai
Traditional nonparametric estimation methods often lead to a slow convergence rate in large dimensions and require unrealistically enormous sizes of datasets for reliable conclusio…
On Large Batch Training and Sharp Minima: A Fokker-Planck Perspective
Xiaowu Dai, Yuhua Zhu
We study the statistical properties of the dynamic trajectory of stochastic gradient descent (SGD). We approximate the mini-batch SGD and the momentum SGD as stochastic differentia…
Post-Regularization Confidence Bands for Ordinary Differential Equations
Xiaowu Dai, Lexin Li
Ordinary differential equation (ODE) is an important tool to study the dynamics of a system of biological and physical processes. A central question in ODE modeling is to infer the…
Kernel Knockoffs Selection for Nonparametric Additive Models
Xiaowu Dai, Xiang Lyu, Lexin Li
Thanks to its fine balance between model flexibility and interpretability, the nonparametric additive model has been widely used, and variable selection for this type of model has…
A resampling approach for causal inference on novel two-point time-series with application to identify risk factors for type-2 diabetes and cardiovascular disease
Xiaowu Dai, Saad Mouti, Marjorie Lima do Vale +3
Two-point time-series data, characterized by baseline and follow-up observations, are frequently encountered in health research. We study a novel two-point time series structure wi…
Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis
Xiaowu Dai, Lexin Li
Multimodal imaging has transformed neuroscience research. While it presents unprecedented opportunities, it also imposes serious challenges. Particularly, it is difficult to combin…
Learning in Multi-Stage Decentralized Matching Markets
Xiaowu Dai, Michael I. Jordan
Matching markets are often organized in a multi-stage and decentralized manner. Moreover, participants in real-world matching markets often have uncertain preferences. This article…
Learning Strategies in Decentralized Matching Markets under Uncertain Preferences
Xiaowu Dai, Michael I. Jordan
We study the problem of decision-making in the setting of a scarcity of shared resources when the preferences of agents are unknown a priori and must be learned from data. Taking t…
Kernel Ordinary Differential Equations
Xiaowu Dai, Lexin Li
Ordinary differential equation (ODE) is widely used in modeling biological and physical processes in science. In this article, we propose a new reproducing kernel-based approach fo…
Towards Theoretical Understanding of Large Batch Training in Stochastic Gradient Descent
Xiaowu Dai, Yuhua Zhu
Stochastic gradient descent (SGD) is almost ubiquitously used for training non-convex optimization tasks. Recently, a hypothesis proposed by Keskar et al. [2017] that large batch m…
High-Dimensional Smoothing Splines and Application in Alzheimer's Disease Prediction Using Magnetic Resonance Imaging
Xiaowu Dai
Recent evidence has shown that structural magnetic resonance imaging (MRI) is an effective tool for Alzheimer's disease (AD) prediction and diagnosis. While traditional MRI-based d…
Selection and Estimation Optimality in High Dimensions with the TWIN Penalty
Xiaowu Dai, Jared D. Huling
We introduce a novel class of variable selection penalties called TWIN, which provides sensible data-adaptive penalization. Under a linear sparsity regime and random Gaussian desig…
Another Look at Statistical Calibration: A Non-Asymptotic Theory and Prediction-Oriented Optimality
Xiaowu Dai, Peter Chien
We provide another look at the statistical calibration problem in computer models. This viewpoint is inspired by two overarching practical considerations of computer models: (i) ma…
Minimax Optimal Rates of Estimation in Functional ANOVA Models with Derivatives
Xiaowu Dai, Peter Chien
We establish minimax optimal rates of convergence for nonparametric estimation in functional ANOVA models when data from first-order partial derivatives are available. Our results…