Search Results for author: Yong Cai

Found 16 papers, 3 papers with code

FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning

no code implementations25 Mar 2024 Xinyuan Ji, Zhaowei Zhu, Wei Xi, Olga Gadyatskaya, Zilong Song, Yong Cai, Yang Liu

The high loss incurred by client-specific samples in heterogeneous label noise poses challenges for distinguishing between client-specific and noisy label samples, impacting the effectiveness of existing label noise learning approaches.

Federated Learning

Identifying Socially Disruptive Policies

1 code implementation26 Jun 2023 Eric Auerbach, Yong Cai

Social disruption occurs when a policy creates or destroys many network connections between agents.

Towards Trustworthy Explanation: On Causal Rationalization

1 code implementation25 Jun 2023 Wenbo Zhang, Tong Wu, Yunlong Wang, Yong Cai, Hengrui Cai

With recent advances in natural language processing, rationalization becomes an essential self-explaining diagram to disentangle the black box by selecting a subset of input texts to account for the major variation in prediction.

Causal Inference

Linear Regression with Centrality Measures

no code implementations18 Oct 2022 Yong Cai

(2) We develop distributional theory for OLS estimators under measurement error and sparsity, finding that OLS estimators are subject to asymptotic bias even when they are consistent.

regression

FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR

no code implementations11 Jul 2022 Luning Bi, Yunlong Wang, Fan Zhang, Zhuqing Liu, Yong Cai, Emily Zhao

In the past decade, with the development of big data technology, an increasing amount of patient information has been stored as electronic health records (EHRs).

Graph Attention Recommendation Systems

On the Performance of the Neyman Allocation with Small Pilots

no code implementations9 Jun 2022 Yong Cai, Ahnaf Rafi

The Neyman Allocation is used in many papers on experimental design, which typically assume that researchers have access to large pilot studies.

Experimental Design

Heterogeneous Treatment Effects for Networks, Panels, and other Outcome Matrices

1 code implementation2 May 2022 Eric Auerbach, Yong Cai

We then propose a new matrix analog of quantile treatment effects that is given by a difference in the eigenvalues.

Experimental Design

Panel Data with Unknown Clusters

no code implementations10 Jun 2021 Yong Cai

We show that our procedure recovers the true clusters with high probability with no assumptions on the cluster structure.

A Modified Randomization Test for the Level of Clustering

no code implementations3 May 2021 Yong Cai

However, a researcher that has chosen to cluster at the county level may be unsure of their decision, given knowledge that observations are independent across states.

Clustering

Some Finite Sample Properties of the Sign Test

no code implementations2 Mar 2021 Yong Cai

Second, we provide a simple theoretical counterexample to show that the sign test over-rejects when the data exhibits correlation.

On the implementation of Approximate Randomization Tests in Linear Models with a Small Number of Clusters

no code implementations17 Feb 2021 Yong Cai, Ivan A. Canay, Deborah Kim, Azeem M. Shaikh

This paper provides a user's guide to the general theory of approximate randomization tests developed in Canay, Romano, and Shaikh (2017) when specialized to linear regressions with clustered data.

Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model

no code implementations1 Jul 2019 Fan Zhang, Tong Wu, Yunlong Wang, Yong Cai, Cao Xiao, Emily Zhao, Lucas Glass, Jimeng Sun

Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare.

Time Series Time Series Analysis

Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks

no code implementations1 Jul 2019 Kezi Yu, Yunlong Wang, Yong Cai, Cao Xiao, Emily Zhao, Lucas Glass, Jimeng Sun

Rare diseases affecting 350 million individuals are commonly associated with delay in diagnosis or misdiagnosis.

Semi-supervised Rare Disease Detection Using Generative Adversarial Network

no code implementations3 Dec 2018 Wenyuan Li, Yunlong Wang, Yong Cai, Corey Arnold, Emily Zhao, Yilian Yuan

Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures.

Generative Adversarial Network

Rare Disease Physician Targeting: A Factor Graph Approach

no code implementations19 Jan 2017 Yong Cai, Yunlong Wang, Dong Dai

For a specified rare disease, only a small number of patients are affected and a fractional of physicians are involved.

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