Yue He

JBHI Journal 2026 Journal Article

$\text{P}^\text{2}$RS: A Quantitative Rating Scale for Pain Assessment based on Pulse Wave Characterization

• Yue He
• Yi Sun
• Ke Sun
• Wei Bin
• Quan Wang
• Heng Yang
• Xinxin Li

For pain intensity assessment, currently there are mainly 11 rating scales, from primitive Visual Analog Scale (VAS) to elaborate Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). However, they all depend on a self-report mechanism, making their results so subjective that the consistency, comparability and reference value are barely satisfactory. Inspired by the phenomenon that discomfort may give rise to the throbbing of radial artery, we develop an objective rating scale innovatively, quantifying the severity of pain by the degree of “lateral instability” of an arterial pulse wave. In attempting to monitor this lateral instability, a sort of ultra-small piezoresistive pressure sensor is fabricated in an area of 0. 4 × 0. 4 $\text{mm}^\text{2}$. With 18 of such sensors, we build a flexible tactile sensing dense-array with a pitch of only 0. 65 mm. Overlying the radial artery perpendicularly to the blood flow direction, the dense-array succeeds in observing the cross-section of a pulse wave. The barycenter of the cross-section of each wave cycle is taken as the feature point to represent its lateral shape and drift. The standard deviation of the barycenters' horizontal coordinates is thereby calculated as the pulsatile perceptual rating scale ( $\text{P}^\text{2}$ RS) to reflect the degree of lateral instability, that is, our scale of pain intensity. Among 86 clinical samples, the pain threshold is 0. 11, which is concluded by a binary classification model based on a support vector machine. In terms of its consistency with previous rating scales, the average correlation coefficient reaches 0. 804 among 43 pain samples.

AAAI Conference 2026 Conference Paper

Error Slice Discovery via Manifold Compactness

• Han Yu
• Hao Zou
• Jiashuo Liu
• Renzhe Xu
• Yue He
• Xingxuan Zhang
• Peng Cui

Despite the great performance of deep learning models in many areas, they still make mistakes and underperform on certain subsets of data, i.e. error slices. Given a trained model, it is important to identify its semantically coherent error slices that are easy to interpret, which is referred to as the error slice discovery problem. However, there is no proper metric of slice coherence without relying on extra information like predefined slice labels. Current evaluation of slice coherence requires access to predefined slices formulated by metadata like attributes or subclasses. Its validity heavily relies on the quality and abundance of metadata, where some possible patterns could be ignored. Besides, current algorithms cannot directly incorporate the constraint of coherence into their optimization objective due to absence of an explicit coherence metric, which could potentially hinder their effectiveness. In this paper, we propose manifold compactness, a coherence metric without reliance on extra information by incorporating the data geometry property into its design, and experiments on typical datasets empirically validate the rationality of the metric. Then we develop Manifold Compactness based error Slice Discovery (MCSD), a novel algorithm that directly treats risk and coherence as the optimization objective, and is flexible to be applied to models of various tasks. Extensive experiments on the benchmark and case studies on other typical datasets demonstrate the superiority of MCSD.

AAAI Conference 2026 Conference Paper

Generating Risky Samples with Conformity Constraints via Diffusion Models

• Han Yu
• Hao Zou
• Xingxuan Zhang
• Zhengyi Wang
• Yue He
• Kehan Li
• Peng Cui

Although neural networks achieve promising performance in many tasks, they may still fail when encountering some examples and bring about risks to applications. To discover risky samples, previous literature attempts to search for patterns of risky samples within existing datasets or inject perturbation into them. Yet in this way the diversity of risky samples is limited by the coverage of existing datasets. To overcome this limitation, recent works adopt diffusion models to produce new risky samples beyond the coverage of existing datasets. However, these methods struggle in the conformity between generated samples and expected categories, which could introduce label noise and severely limit their effectiveness in applications. To address this issue, we propose RiskyDiff that incorporates the embeddings of both texts and images as implicit constraints of category conformity. We also design a conformity score to further explicitly strengthen the category conformity, as well as introduce the mechanisms of embedding screening and risky gradient guidance to boost the risk of generated samples. Extensive experiments reveal that RiskyDiff greatly outperforms existing methods in terms of the degree of risk, generation quality, and conformity with conditioned categories. We also empirically show the generalization ability of the models can be enhanced by augmenting training data with generated samples of high conformity.

YNIMG Journal 2026 Journal Article

Neural-molecular signatures of insomnia: Insights from signed differential mapping and gene expression analysis

• Ruisi Wang
• Yuwen He
• Andriy Myachykov
• Chan Tat Leong
• Wenyue Gao
• Cheneryue Zhang
• Yue He
• Zhen Yuan

NeurIPS Conference 2025 Conference Paper

Environment Inference for Learning Generalizable Dynamical System

• Shixuan Liu
• Yue He
• Haotian Wang
• Wenjing Yang
• Yunfei Wang
• Peng Cui
• Zhong Liu

Data-driven methods offer efficient and robust solutions for analyzing complex dynamical systems but rely on the assumption of I. I. D. data, driving the development of generalization techniques for handling environmental differences. These techniques, however, are limited by their dependence on environment labels, which are often unavailable during training due to data acquisition challenges, privacy concerns, and environmental variability, particularly in large public datasets and privacy-sensitive domains. In response, we propose DynaInfer, a novel method that infers environment specifications by analyzing prediction errors from fixed neural networks within each training round, enabling environment assignments directly from data. We prove our algorithm effectively solves the alternating optimization problem in unlabeled scenarios and validate it through extensive experiments across diverse dynamical systems. Results show that DynaInfer outperforms existing environment assignment techniques, converges rapidly to true labels, and even achieves superior performance when environment labels are available.

AAAI Conference 2024 Conference Paper

Full Bayesian Significance Testing for Neural Networks

• Zehua Liu
• Zimeng Li
• Jingyuan Wang
• Yue He

Significance testing aims to determine whether a proposition about the population distribution is the truth or not given observations. However, traditional significance testing often needs to derive the distribution of the testing statistic, failing to deal with complex nonlinear relationships. In this paper, we propose to conduct Full Bayesian Significance Testing for neural networks, called nFBST, to overcome the limitation in relationship characterization of traditional approaches. A Bayesian neural network is utilized to fit the nonlinear and multi-dimensional relationships with small errors and avoid hard theoretical derivation by computing the evidence value. Besides, nFBST can test not only global significance but also local and instance-wise significance, which previous testing methods don't focus on. Moreover, nFBST is a general framework that can be extended based on the measures selected, such as Grad-nFBST, LRP-nFBST, DeepLIFT-nFBST, LIME-nFBST. A range of experiments on both simulated and real data are conducted to show the advantages of our method.

IJCAI Conference 2024 Conference Paper

Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting

• Zehua Liu
• Jingyuan Wang
• Zimeng Li
• Yue He

Due to the complex and dynamic traffic contexts, the interpretability and uncertainty of traffic forecasting have gained increasing attention. Significance testing is a powerful tool in statistics used to determine whether a hypothesis is valid, facilitating the identification of pivotal features that predominantly contribute to the true relationship. However, existing works mainly regard traffic forecasting as a deterministic problem, making it challenging to perform effective significance testing. To fill this gap, we propose to conduct Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting, namely ST-nFBST. A Bayesian neural network is utilized to capture the complicated traffic relationships through an optimization function resolved in the context of aleatoric uncertainty and epistemic uncertainty. Thereupon, ST-nFBST can achieve the significance testing by means of a delicate grad-based evidence value, further capturing the inherent traffic schema for better spatiotemporal modeling. Extensive experiments are conducted on METR-LA and PEMS-BAY to verify the advantages of our method in terms of uncertainty analysis and significance testing, helping the interpretability and promotion of traffic forecasting.

AIIM Journal 2024 Journal Article

RM-GPT: Enhance the comprehensive generative ability of molecular GPT model via LocalRNN and RealFormer

• Wenfeng Fan
• Yue He
• Fei Zhu

AAAI Conference 2023 Conference Paper

Covariate-Shift Generalization via Random Sample Weighting

• Yue He
• Xinwei Shen
• Renzhe Xu
• Tong Zhang
• Yong Jiang
• Wenchao Zou
• Peng Cui

Shifts in the marginal distribution of covariates from training to the test phase, named covariate-shifts, often lead to unstable prediction performance across agnostic testing data, especially under model misspecification. Recent literature on invariant learning attempts to learn an invariant predictor from heterogeneous environments. However, the performance of the learned predictor depends heavily on the availability and quality of provided environments. In this paper, we propose a simple and effective non-parametric method for generating heterogeneous environments via Random Sample Weighting (RSW). Given the training dataset from a single source environment, we randomly generate a set of covariate-determining sample weights and use each weighted training distribution to simulate an environment. We theoretically show that under appropriate conditions, such random sample weighting can produce sufficient heterogeneity to be exploited by common invariance constraints to find the invariant variables for stable prediction under covariate shifts. Extensive experiments on both simulated and real-world datasets clearly validate the effectiveness of our method.

EAAI Journal 2023 Journal Article

Quantized kernel recursive minimum error entropy algorithm

• Wang Jiang
• Yuyi Gao
• Yue He
• Shanmou Chen

AAAI Conference 2023 Conference Paper

Stable Learning via Sparse Variable Independence

• Han Yu
• Peng Cui
• Yue He
• Zheyan Shen
• Yong Lin
• Renzhe Xu
• Xingxuan Zhang

The problem of covariate-shift generalization has attracted intensive research attention. Previous stable learning algorithms employ sample reweighting schemes to decorrelate the covariates when there is no explicit domain information about training data. However, with finite samples, it is difficult to achieve the desirable weights that ensure perfect independence to get rid of the unstable variables. Besides, decorrelating within stable variables may bring about high variance of learned models because of the over-reduced effective sample size. A tremendous sample size is required for these algorithms to work. In this paper, with theoretical justification, we propose SVI (Sparse Variable Independence) for the covariate-shift generalization problem. We introduce sparsity constraint to compensate for the imperfectness of sample reweighting under the finite-sample setting in previous methods. Furthermore, we organically combine independence-based sample reweighting and sparsity-based variable selection in an iterative way to avoid decorrelating within stable variables, increasing the effective sample size to alleviate variance inflation. Experiments on both synthetic and real-world datasets demonstrate the improvement of covariate-shift generalization performance brought by SVI.

AAAI Conference 2023 Conference Paper

TransVCL: Attention-Enhanced Video Copy Localization Network with Flexible Supervision

• Sifeng He
• Yue He
• Minlong Lu
• Chen Jiang
• Xudong Yang
• Feng Qian
• Xiaobo Zhang
• Lei Yang

Video copy localization aims to precisely localize all the copied segments within a pair of untrimmed videos in video retrieval applications. Previous methods typically start from frame-to-frame similarity matrix generated by cosine similarity between frame-level features of the input video pair, and then detect and refine the boundaries of copied segments on similarity matrix under temporal constraints. In this paper, we propose TransVCL: an attention-enhanced video copy localization network, which is optimized directly from initial frame-level features and trained end-to-end with three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for similarity matrix generation, and a temporal alignment module for copied segments localization. In contrast to previous methods demanding the handcrafted similarity matrix, TransVCL incorporates long-range temporal information between feature sequence pair using self- and cross- attention layers. With the joint design and optimization of three components, the similarity matrix can be learned to present more discriminative copied patterns, leading to significant improvements over previous methods on segment-level labeled datasets (VCSL and VCDB). Besides the state-of-the-art performance in fully supervised setting, the attention architecture facilitates TransVCL to further exploit unlabeled or simply video-level labeled data. Additional experiments of supplementing video-level labeled datasets including SVD and FIVR reveal the high flexibility of TransVCL from full supervision to semi-supervision (with or without video-level annotation). Code is publicly available at https://github.com/transvcl/TransVCL.

AAAI Conference 2022 Conference Paper

Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition

• Yue He
• Chen Chen
• Jing Zhang
• Juhua Liu
• Fengxiang He
• Chaoyue Wang
• Bo Du

Existing Scene Text Recognition (STR) methods typically use a language model to optimize the joint probability of the 1D character sequence predicted by a visual recognition (VR) model, which ignore the 2D spatial context of visual semantics within and between character instances, making them not generalize well to arbitrary shape scene text. To address this issue, we make the first attempt to perform textual reasoning based on visual semantics in this paper. Technically, given the character segmentation maps predicted by a VR model, we construct a subgraph for each instance, where nodes represent the pixels in it and edges are added between nodes based on their spatial similarity. Then, these subgraphs are sequentially connected by their root nodes and merged into a complete graph. Based on this graph, we devise a graph convolutional network for textual reasoning (GTR) by supervising it with a cross-entropy loss. GTR can be easily plugged in representative STR models to improve their performance owing to better textual reasoning. Specifically, we construct our model, namely S-GTR, by paralleling GTR to the language model in a segmentation-based STR baseline, which can effectively exploit the visual-linguistic complementarity via mutual learning. S-GTR sets new state-of-the-art on six challenging STR benchmarks and generalizes well to multi-linguistic datasets. Code is available at https: //github. com/adeline-cs/GTR.

NeurIPS Conference 2020 Conference Paper

Counterfactual Prediction for Bundle Treatment

• Hao Zou
• Peng Cui
• Bo Li
• Zheyan Shen
• Jianxin Ma
• Hongxia Yang
• Yue He

Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields. Among the various forms of treatment specification, bundle treatment has been widely adopted in many scenarios, such as recommendation systems and online marketing. The bundle treatment usually can be abstracted as a high dimensional binary vector, which makes it more challenging for researchers to remove the confounding bias in observational data. In this work, we assume the existence of low dimensional latent structure underlying bundle treatment. Via the learned latent representations of treatments, we propose a novel variational sample re-weighting (VSR) method to eliminate confounding bias by decorrelating the treatments and confounders. Finally, we conduct extensive experiments to demonstrate that the predictive model trained on this re-weighted dataset can achieve more accurate counterfactual outcome prediction.

AIIM Journal 2020 Journal Article

DESIGN AND DEVELOPMENT OF HUMAN COMPUTER INTERFACE USING ELECTROOCULOGRAM WITH DEEP LEARNING

• Geer Teng
• Yue He
• Hengjun Zhao
• Dunhu Liu
• Jin Xiao
• S. Ramkumar

AAAI Conference 2018 Conference Paper

Merge or Not? Learning to Group Faces via Imitation Learning

• Yue He
• Kaidi Cao
• Cheng Li
• Chen Loy

Face grouping remains a challenging problem despite the remarkable capability of deep learning approaches in learning face representation. In particular, grouping results can still be egregious given profile faces and a large number of uninteresting faces and noisy detections. Often, a user needs to correct the erroneous grouping manually. In this study, we formulate a novel face grouping framework that learns clustering strategy from ground-truth simulated behavior. This is achieved through imitation learning (a. k. a apprenticeship learning or learning by watching) via inverse reinforcement learning (IRL). In contrast to existing clustering approaches that group instances by similarity, our framework makes sequential decision to dynamically decide when to merge two face instances/groups driven by short- and long-term rewards. Extensive experiments on three benchmark datasets show that our framework outperforms unsupervised and supervised baselines.

IJCAI Conference 2018 Conference Paper

Progressive Generative Hashing for Image Retrieval

• Yuqing Ma
• Yue He
• Fan Ding
• Sheng Hu
• Jun Li
• Xianglong Liu

Recent years have witnessed the success of the emerging hashing techniques in large-scale image retrieval. Owing to the great learning capacity, deep hashing has become one of the most promising solutions, and achieved attractive performance in practice. However, without semantic label information, the unsupervised deep hashing still remains an open question. In this paper, we propose a novel progressive generative hashing (PGH) framework to help learn a discriminative hashing network in an unsupervised way. Very different from existing studies, it first treats the hash codes as a kind of semantic condition for the similar image generation, and simultaneously feeds the original image and its codes into the generative adversarial networks (GANs). The real images together with the synthetic ones can further help train a discriminative hashing network based on a triplet loss. By iteratively inputting the learnt codes into the hash conditioned GANs, we can progressively enable the hashing network to discover the semantic relations. Extensive experiments on the widely-used image datasets demonstrate that PGH can significantly outperforms state-of-the-art unsupervised hashing methods.