Search Results for author: Yunyi Li

Found 9 papers, 0 papers with code

Mitigating Label Bias via Decoupled Confident Learning

no code implementations18 Jul 2023 Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky

While the presence of labeling bias has been discussed conceptually, there is a lack of methodologies to address this problem.

Fairness Hate Speech Detection

More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias

no code implementations15 Jul 2022 Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky

We then empirically show that, when overlooking label bias, collecting more data can aggravate bias, and imposing fairness constraints that rely on the observed labels in the data collection process may not address the problem.

Active Learning Fairness

Nonconvex ${L_ {1/2}} $-Regularized Nonlocal Self-similarity Denoiser for Compressive Sensing based CT Reconstruction

no code implementations15 May 2022 Yunyi Li, Yiqiu Jiang, Hengmin Zhang, Jianxun Liu, Xiangling Ding, Guan Gui

In this paper, we develop a ${{L_ {{1/2}}}} $-regularized nonlocal self-similarity (NSS) denoiser for CT reconstruction problem, which integrates low-rank approximation with group sparse coding (GSC) framework.

Compressive Sensing Computed Tomography (CT) +1

Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation

no code implementations20 Nov 2021 Yunyi Li, Pengpeng Zhao, Guanfeng Liu, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Xiaofang Zhou

In this paper, we propose an Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) model to capture the relation information between items for global item representation and local user intention learning.

Sequential Recommendation

Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning

no code implementations6 May 2021 Tong Wang, Jingyi Yang, Yunyi Li, Boxiang Wang

We propose Partially Interpretable Estimators (PIE) which attribute a prediction to individual features via an interpretable model, while a (possibly) small part of the PIE prediction is attributed to the interaction of features via a black-box model, with the goal to boost the predictive performance while maintaining interpretability.

Attribute BIG-bench Machine Learning +1

From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration

no code implementations10 Jul 2019 Yunyi Li, Guan Gui, Xiefeng Cheng

Recently, low-rank matrix recovery theory has been emerging as a significant progress for various image processing problems.

Compressive Sensing Deblurring +3

ADMM-IDNN: Iteratively Double-reweighted Nuclear Norm Algorithm for Group-prior based Nonconvex Compressed Sensing via ADMM

no code implementations23 Mar 2019 Yunyi Li, Fei Dai, Yu Zhao, Xiefeng Cheng, Guan Gui

Group-prior based regularization method has led to great successes in various image processing tasks, which can usually be considered as a low-rank matrix minimization problem.

Image and Video Processing

Next Hit Predictor - Self-exciting Risk Modeling for Predicting Next Locations of Serial Crimes

no code implementations13 Dec 2018 Yunyi Li, Tong Wang

Our goal is to predict the location of the next crime in a crime series, based on the identified previous offenses in the series.

Cannot find the paper you are looking for? You can Submit a new open access paper.