Bing‐Yi Jing
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Author Swipe
View article: Estimation in linear models with clustered data
Estimation in linear models with clustered data Open
We study linear regression models with clustered data, high-dimensional controls, and a complicated structure of exclusion restrictions. We propose a correctly centered internal IV estimator that accommodates a variety of exclusion restric…
View article: Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score
Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score Open
Conformal prediction (CP) is a powerful framework for uncertainty quantification, providing prediction sets with coverage guarantees when calibrated on sufficient labeled data. However, in real-world applications where labeled data is ofte…
View article: Leveraging Shared Factor Structures for Enhanced Matrix Completion with Nonconvex Penalty Regularization
Leveraging Shared Factor Structures for Enhanced Matrix Completion with Nonconvex Penalty Regularization Open
This article investigates the problem of noisy low-rank matrix completion with a shared factor structure, leveraging the auxiliary information from the missing indicator matrix to enhance prediction accuracy. Despite decades of development…
View article: A plug and play fuzzy mask extraction module for single image deraining
A plug and play fuzzy mask extraction module for single image deraining Open
In this paper, a plug and play fuzzy mask extraction module for single image rain streak removal is proposed. Specifically, fuzzy mask maps of the rain data-set are obtained by optimizing the convex combination of several grouping function…
View article: Exploring Imbalanced Annotations for Effective In-Context Learning
Exploring Imbalanced Annotations for Effective In-Context Learning Open
Large language models (LLMs) have shown impressive performance on downstream tasks through in-context learning (ICL), which heavily relies on the demonstrations selected from annotated datasets. However, these datasets often exhibit long-t…
View article: Parametric Scaling Law of Tuning Bias in Conformal Prediction
Parametric Scaling Law of Tuning Bias in Conformal Prediction Open
Conformal prediction is a popular framework of uncertainty quantification that constructs prediction sets with coverage guarantees. To uphold the exchangeability assumption, many conformal prediction methods necessitate an additional holdo…
View article: Two-way Node Popularity Model for Directed and Bipartite Networks
Two-way Node Popularity Model for Directed and Bipartite Networks Open
There has been extensive research on community detection in directed and bipartite networks. However, these studies often fail to consider the popularity of nodes in different communities, which is a common phenomenon in real-world network…
View article: Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model
Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model Open
``Distribution shift'' is the main obstacle to the success of offline reinforcement learning. A learning policy may take actions beyond the behavior policy's knowledge, referred to as Out-of-Distribution (OOD) actions. The Q-values for the…
View article: ChineseSafe: A Chinese Benchmark for Evaluating Safety in Large Language Models
ChineseSafe: A Chinese Benchmark for Evaluating Safety in Large Language Models Open
With the rapid development of Large language models (LLMs), understanding the capabilities of LLMs in identifying unsafe content has become increasingly important. While previous works have introduced several benchmarks to evaluate the saf…
View article: Fine-tuning can Help Detect Pretraining Data from Large Language Models
Fine-tuning can Help Detect Pretraining Data from Large Language Models Open
In the era of large language models (LLMs), detecting pretraining data has been increasingly important due to concerns about fair evaluation and ethical risks. Current methods differentiate members and non-members by designing scoring func…
View article: A Plug-and-Play Fuzzy Mask Extraction Module for Single Image Deraining
A Plug-and-Play Fuzzy Mask Extraction Module for Single Image Deraining Open
The learning of rain masks is important for finely extracting the rain streaks and helping the recovery of rain-removed images.However, the rain mask attention blocks involved in exisiting methods cannot be directly applied to other derain…
View article: Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning
Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning Open
In offline reinforcement learning, it is necessary to manage out-of-distribution actions to prevent overestimation of value functions. One class of methods, the policy-regularized method, addresses this problem by constraining the target p…
View article: Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution Open
Knowledge distillation (KD) is a promising yet challenging model compression technique that transfers rich learning representations from a well-performing but cumbersome teacher model to a compact student model. Previous methods for image …
View article: Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems
Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems Open
In implicit collaborative filtering, hard negative mining techniques are developed to accelerate and enhance the recommendation model learning. However, the inadvertent selection of false negatives remains a major concern in hard negative …
View article: Exploring Learning Complexity for Efficient Downstream Dataset Pruning
Exploring Learning Complexity for Efficient Downstream Dataset Pruning Open
The ever-increasing fine-tuning cost of large-scale pre-trained models gives rise to the importance of dataset pruning, which aims to reduce dataset size while maintaining task performance. However, existing dataset pruning methods require…
View article: PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction
PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction Open
Motivation Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating peptides (CPPs). These peptides are gaining prominence due to their ability to penetrate eukary…
View article: Data Upcycling Knowledge Distillation for Image Super-Resolution
Data Upcycling Knowledge Distillation for Image Super-Resolution Open
Knowledge distillation (KD) compresses deep neural networks by transferring task-related knowledge from cumbersome pre-trained teacher models to compact student models. However, current KD methods for super-resolution (SR) networks overloo…
View article: Constrained Policy Optimization with Explicit Behavior Density for Offline Reinforcement Learning
Constrained Policy Optimization with Explicit Behavior Density for Offline Reinforcement Learning Open
Due to the inability to interact with the environment, offline reinforcement learning (RL) methods face the challenge of estimating the Out-of-Distribution (OOD) points. Existing methods for addressing this issue either control policy to e…
View article: Joint Semantic Deep Learning Algorithm for Object Detection under Foggy Road Conditions
Joint Semantic Deep Learning Algorithm for Object Detection under Foggy Road Conditions Open
Current mainstream deep learning methods for object detection are generally trained on high-quality datasets, which might have inferior performances under bad weather conditions. In the paper, a joint semantic deep learning algorithm is pr…
View article: Enhancing Recommender Systems: A Strategy to Mitigate False Negative Impact
Enhancing Recommender Systems: A Strategy to Mitigate False Negative Impact Open
In implicit collaborative filtering (CF) task of recommender systems, recent works mainly focus on model structure design with promising techniques like graph neural networks (GNNs). Effective and efficient negative sampling methods that s…
View article: Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions
Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions Open
Color is an important feature of vehicles, and it plays a key role in intelligent traffic management and criminal investigation. Existing algorithms for vehicle color recognition are typically trained on data under good weather conditions …
View article: Rain Rendering and Construction of Rain Vehicle Color-24 Dataset
Rain Rendering and Construction of Rain Vehicle Color-24 Dataset Open
The fine identification of vehicle color can assist in criminal investigation or intelligent traffic management law enforcement. Since almost all vehicle-color datasets that are used to train models are collected in good weather, the exist…
View article: Subsampling Spectral Clustering for Large-Scale Social Networks
Subsampling Spectral Clustering for Large-Scale Social Networks Open
Online social network platforms such as Twitter and Sina Weibo have been extremely popular over the past 20 years. Identifying the network community of a social platform is essential to exploring and understanding the users' interests. How…
View article: Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression
Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression Open
In this work, we investigate Gaussian process regression used to recover a function based on noisy observations. We derive upper and lower error bounds for Gaussian process regression with possibly misspecified correlation functions. The o…
View article: Crawling subsampling for multivariate spatial autoregression model in large-scale networks
Crawling subsampling for multivariate spatial autoregression model in large-scale networks Open
In network data analysis, multivariate spatial autoregression (MSAR) models may be used to analyze the autocorrelation among multiple responses. With large-scale networks, the estimation for MSAR on the entire network is computationally ex…