Chenyang Xu

EAAI Journal 2026 Journal Article

Depression detection via multimodal analysis using a large language model-powered interview platform

• Yangbin Chen
• Chenyang Xu
• Chunfeng Liang
• Yanbao Tao
• Fan Du
• Xiaofeng Liu
• Mingqia Wang
• Zhuoran Li

The global prevalence of Major Depressive Disorder (MDD) increases annually, establishing it as one of the leading contributors to the global burden of diseases. Traditional symptom-based methods, such as face-to-face interviews between psychiatrists and patients, are widely used for detecting and assessing depression. However, these methods are time-consuming which often require several tens of minutes per interview. While the patient population is growing, there is a significant shortage of qualified clinicians, especially in low- and middle-income communities. Furthermore, the subjectivity in human assessments leads to variability in diagnostic results among different clinicians. In this study, we aim to address these challenges by developing objective, efficient, and reliable Artificial Intelligence (AI) tools and methods for the detection and assessment of MDD. Specifically, we have developed a human–computer interview platform, powered by Large Language Models (LLMs) equipped with knowledge from the Hamilton Rating Scale for Depression (HAMD). Additionally, we proposed multimodal topic-aware deep learning models that are trained on text, audio, and video data to detect MDD, assess depression severity, and predict HAMD scores. Data were gathered through human–computer interviews with 346 participants recruited from psychiatric outpatient clinics and community settings. The experimental results demonstrate that our platform achieves diagnostic performance comparable to that of trained clinicians.

AAMAS Conference 2026 Conference Paper

Multiagent Matroid Upgrading: Greedy is Fair and Efficient

• Qingwen Ma
• Chao Peng
• Changfeng Xu
• Chenyang Xu
• Ruilong Zhang

This paper introduces a general multiagent matroid upgrading problem that models a broad class of real-world resource allocation tasks. In this setting, there are multiple agents and a ground set of elements, where each element is assigned to a specific agent and has two associated costs: a default cost and a reduced (upgraded) cost. Upgrading an element lowers its cost to the upgraded value, while non-upgraded elements retain their default costs. Each agent is associated with its own matroid, with the goal of finding a minimum-cost basis. The central task is to select at most𝑘 elements to upgrade so as to minimize a non-decreasing convex function over the agents’ minimum basis costs, capturing both efficiency and fairness objectives in multiagent systems. We show that the problem is polynomial-time solvable and that an optimal solution can be obtained via a simple greedy algorithm. Our analysis exploits the structural properties of matroids to establish the existence of optimal substructures, thereby ensuring that greedy upgrading yields optimal outcomes. Building on this insight, we can further extend our result to more general settings, such as scenarios with interval fairness constraints, where the number of elements upgraded for each agent is required to lie within a specified interval.

AAAI Conference 2026 Conference Paper

Richer Representations for Neural Algorithmic Reasoning via Auxiliary Reconstruction

• Jiafu Huang
• Chao Peng
• Chenyang Xu
• Zhengfeng Yang
• Kecheng Cai
• Chenhao Zhang
• Yi Wang
• Yiwei Gong

Neural algorithmic reasoning has recently emerged as a popular research direction. It aims to train neural networks to mimic the step-by-step behavior of classical rule-based algorithms. More specifically, the execution of such algorithms can be abstracted as a sequence of states, where each state represents the intermediate outcome after an execution step. The training objective is to generate state sequences that replicate the underlying algorithmic process. A common framework for this task adopts an ``encoder-processor-decoder'' architecture, where the encoder learns representations of states, the processor simulates algorithmic steps, and the decoder reconstructs output states. While prior work has primarily focused on improving the processor, the role of the encoder in representation learning has received little attention. Most existing methods rely on simple MLP encoders, raising the question of whether such representations are sufficiently informative for supporting algorithmic reasoning. This paper investigates how to improve encoder representations for neural algorithmic reasoning. We propose a reconstruction module that aims to recover the input state from its encoded representation. This auxiliary reconstruction task encourages the encoder to retain critical information about the input. We demonstrate that incorporating this task during training improves the performance of existing neural architectures on standard benchmarks. Furthermore, we observe that current encoders often underutilize the correlations among features within a state. To address this, we draw inspiration from self-supervised learning and design an enhanced variant of the auxiliary task that encourages the encoder to capture intra-state feature dependencies. Experimental results show that our method enables the encoder to learn richer representations, thereby enhancing the performance of existing processors on algorithmic reasoning tasks.

NeurIPS Conference 2025 Conference Paper

A Beyond-Worst-Case Analysis of Greedy k-means++

• Qingyun Chen
• Sungjin Im
• Ben Moseley
• Ryan Milstrey
• Chenyang Xu
• Ruilong Zhang

$k$-means++ and the related greedy $k$-means++ algorithm are celebrated algorithms that efficiently compute seeds for Lloyd's algorithm. Greedy $k$-means++ is a generalization of $k$-means++ where, in each iteration, a new seed is greedily chosen among multiple $\ell \geq 2$ points sampled, as opposed to a single seed being sampled in $k$-means++. While empirical studies consistently show the superior performance of greedy $k$-means++, making it a preferred method in practice, a discrepancy exists between theory and practice. No theoretical justification currently explains this improved performance. Indeed, the prevailing theory suggests that greedy $k$-means++ exhibits worse performance than $k$-means++ in worst-case scenarios. This paper presents an analysis demonstrating the outperformance of the greedy algorithm compared to $k$-means++ for a natural class of well-separated instances with exponentially decaying distributions, such as Gaussian, specifically when $\ell = \Theta(\log k)$, a common parameter setting in practical applications.

IJCAI Conference 2025 Conference Paper

Fair Submodular Maximization over a Knapsack Constraint

• Lijun Li
• Chenyang Xu
• Liuyi Yang
• Ruilong Zhang

We consider fairness in submodular maximization subject to a knapsack constraint, a fundamental problem with various applications in economics, machine learning, and data mining. In the model, we are given a set of ground elements, each associated with a cost and a color, and a monotone submodular function defined over them. The goal is to maximize the submodular function while guaranteeing that the total cost does not exceed a specified budget (the knapsack constraint) and that the number of elements selected for each color falls within a designated range (the fairness constraint). While there exists some recent literature on this topic, the existence of a non-trivial approximation for the problem -- without relaxing either the knapsack or fairness constraints -- remains a challenging open question. This paper makes progress in this direction. We demonstrate that when the number of colors is constant, there exists a polynomial-time algorithm that achieves a constant approximation with high probability. Additionally, we show that if either the knapsack or fairness constraint is relaxed only to require expected satisfaction, a tight approximation ratio of (1-1/e-epsilon) can be obtained in expectation for any epsilon >0.

IJCAI Conference 2025 Conference Paper

LLM-TPF: Multiscale Temporal Periodicity-Semantic Fusion LLMs for Time Series Forecasting

• Qihong Pan
• Haofei Tan
• Guojiang Shen
• Xiangjie Kong
• Mengmeng Wang
• Chenyang Xu

Large language models have demonstrated remarkable generalization capabilities and strong performance across various fields. Recent research has highlighted their significant potential in time series forecasting. However, time series data often exhibit complex periodic characteristics, posing a substantial challenge in enabling these models to effectively capture latent patterns. To address this challenge, we propose a novel framework, LLM-TPF, which leverages individuality and commonality fusion to enhance time series forecasting. In the frequency domain, periodic features are extracted to reveal the intrinsic periodicity of the data, while textual prototypes are used to indicate temporal trends. In the time domain, carefully designed prompts are employed to guide the models in comprehending global information. A commonality fusion mechanism further aggregates heterogeneous information across dimensions, and three distinct language models are utilized to independently process different types of information. Extensive real-world experiments demonstrate that LLM-TPF is a powerful tool for time series forecasting, achieving superior performance compared to state-of-the-art specialized models and exhibiting exceptional generalization ability in zero-shot scenarios. Code is available at https: //github. com/switchsky/LLM-TPF.

IJCAI Conference 2025 Conference Paper

Logarithmic Approximations for Fair k-Set Selection

• Shi Li
• Chenyang Xu
• Ruilong Zhang

We study the fair k-set selection problem where we aim to select k sets from a given set system such that the (weighted) occurrence times that each element appears in these k selected sets are balanced, i. e. , the maximum (weighted) occurrence times are minimized. By observing that a set system can be formulated into a bipartite graph G: =(L cup R, E), our problem is equivalent to selecting k vertices from R such that the maximum (weighted) number selected neighbors of vertices in L is minimized. The problem arises in a wide range of applications in various fields, such as machine learning, artificial intelligence, and operations research. We first prove that the problem is NP-hard even if the maximum degree Delta of the input bipartite graph is 3, and the problem is in P when Delta=2. We then show that the problem is also in P when the input set system forms a laminar family. Based on intuitive linear programming, we show that two rounding algorithms achieve O(log n/(log log n))-approximation on general bipartite graphs, and an independent rounding algorithm achieves O(log(Delta))-approximation on bipartite graphs with a maximum degree Delta. We demonstrate that our analysis is almost tight by providing a hard instance for this linear programming.

JBHI Journal 2025 Journal Article

PhysCL: Knowledge-Aware Contrastive Learning of Physiological Signal Models for Cuff-Less Blood Pressure Estimation

• Renju Liu
• Jianfei Shen
• Yang Gu
• Yiqiang Chen
• Jiling Zhang
• Qingyu Wu
• Chenyang Xu
• Feiyi Fan

Training deep learning models for photoplethysmography(PPG)-based cuff-less blood pressure estimation often requires a substantial amount of labeled data collected through sophisticated medical instruments, posing significant challenges in practical applications. To address this issue, we propose Physiological Knowledge-Aware Contrastive Learning (PhysCL), a novel approach designed to reduce the dependence on labeled PPG data while improving blood pressure estimation accuracy. Specifically, PhysCL tackles the semantic consistency problem in contrastive learning by introducing a knowledge-aware augmentation bank, which generates positive physiological signal pairs using knowledge-based constraints during the contrastive pair generation. Additionally, we propose a contrastive feature reconstruction method to enhance feature diversity and prevent model collapse through feature re-sampling and re-weighting. We evaluate PhysCL on data from 106 subjects across the MIMIC III, MIMIC IV, and UQVS datasets under cross-dataset validation settings, comparing it against state-of-the-art contrastive learning methods and blood pressure estimation models. PhysCL achieves an average mean absolute error of 9. 5/5. 9 mmHg (systolic/diastolic) across the three datasets, using only 2% labeled data combined with 98% unlabeled data for pre-training and 5 samples for personalization, which represents a 6. 2% /4. 3% improvement, respectively, over the current best supervised methods. The ablation study provides further convincing evidence that the unlabeled data can be utilized to improve the existing cuff-less blood pressure estimation models and shed light on unsupervised contrastive learning for physiological signals.

IJCAI Conference 2024 Conference Paper

A Context-Enhanced Framework for Sequential Graph Reasoning

• Shuo Shi
• Chao Peng
• Chenyang Xu
• Zhengfeng Yang

The paper studies sequential reasoning over graph-structured data, which stands as a fundamental task in various trending fields like automated math problem solving and neural graph algorithm learning, attracting a lot of research interest. Simultaneously managing both sequential and graph-structured information in such tasks presents a notable challenge. Over recent years, many neural architectures in the literature have emerged to tackle the issue. In this work, we generalize the existing architectures and propose a context-enhanced framework. The crucial innovation is that the reasoning of each step does not only rely on the outcome of the preceding step but also leverages the aggregation of information from more historical outcomes. The idea stems from our observation that in sequential graph reasoning, each step's outcome has a much stronger inner connection with each other compared to traditional seq-to-seq tasks. We show that the framework can effectively integrate with the existing methods, enhancing their reasoning abilities. Empirical evaluations are conducted on the challenging CLRS Reasoning Benchmark, and the results demonstrate that the proposed framework significantly improves the performance of existing architectures, yielding state-of-the-art results across the majority of the datasets within the benchmark.

NeurIPS Conference 2024 Conference Paper

Open-Book Neural Algorithmic Reasoning

• Hefei Li
• Chao Peng
• Chenyang Xu
• Zhengfeng Yang

Neural algorithmic reasoning is an emerging area of machine learning that focuses on building neural networks capable of solving complex algorithmic tasks. Recent advancements predominantly follow the standard supervised learning paradigm -- feeding an individual problem instance into the network each time and training it to approximate the execution steps of a classical algorithm. We challenge this mode and propose a novel open-book learning framework. In this framework, whether during training or testing, the network can access and utilize all instances in the training dataset when reasoning for a given instance. Empirical evaluation is conducted on the challenging CLRS Algorithmic Reasoning Benchmark, which consists of 30 diverse algorithmic tasks. Our open-book learning framework exhibits a significant enhancement in neural reasoning capabilities. Further, we notice that there is recent literature suggesting that multi-task training on CLRS can improve the reasoning accuracy of certain tasks, implying intrinsic connections between different algorithmic tasks. We delve into this direction via the open-book framework. When the network reasons for a specific task, we enable it to aggregate information from training instances of other tasks in an attention-based manner. We show that this open-book attention mechanism offers insights into the inherent relationships among various tasks in the benchmark and provides a robust tool for interpretable multi-task training.

AAAI Conference 2024 Conference Paper

Sampling for Beyond-Worst-Case Online Ranking

• Qingyun Chen
• Sungjin Im
• Benjamin Moseley
• Chenyang Xu
• Ruilong Zhang

The feedback arc set problem is one of the most fundamental and well-studied ranking problems where n objects are to be ordered based on their pairwise comparison. The problem enjoys several efficient approximation algorithms in the offline setting. Unfortunately, online there are strong lower bounds on the competitive ratio establishing that no algorithm can perform well in the worst case. This paper introduces a new beyond-worst-case model for online feedback arc set. In the model, a sample of the input is given to the algorithm offline before the remaining instance is revealed online. This models the case in practice where yesterday's data is available and is similar to today's online instance. This sample is drawn from a known distribution which may not be uniform. We design an online algorithm with strong theoretical guarantees. The algorithm has a small constant competitive ratio when the sample is uniform---if not, we show we can recover the same result by adding a provably minimal sample. Empirical results validate the theory and show that such algorithms can be used on temporal data to obtain strong results.

AAAI Conference 2023 Conference Paper

Min-Max Submodular Ranking for Multiple Agents

• Qingyun Chen
• Sungjin Im
• Benjamin Moseley
• Chenyang Xu
• Ruilong Zhang

In the submodular ranking (SR) problem, the input consists of a set of submodular functions defined on a ground set of elements. The goal is to order elements for all the functions to have value above a certain threshold as soon on average as possible, assuming we choose one element per time. The problem is flexible enough to capture various applications in machine learning, including decision trees. This paper considers the min-max version of SR where multiple instances share the ground set. With the view of each instance being associated with an agent, the min-max problem is to order the common elements to minimize the maximum objective of all agents---thus, finding a fair solution for all agents. We give approximation algorithms for this problem and demonstrate their effectiveness in the application of finding a decision tree for multiple agents.

AAAI Conference 2023 Conference Paper

Multiagent MST Cover: Pleasing All Optimally via a Simple Voting Rule

• Bo Li
• Xiaowei Wu
• Chenyang Xu
• Ruilong Zhang

Given a connected graph on whose edges we can build roads to connect the nodes, a number of agents hold possibly different perspectives on which edges should be selected by assigning different edge weights. Our task is to build a minimum number of roads so that every agent has a spanning tree in the built subgraph whose weight is the same as a minimum spanning tree in the original graph. We first show that this problem is NP-hard and does not admit better than ((1-o(1)) ln k)-approximation polynomial-time algorithms unless P = NP, where k is the number of agents. We then give a simple voting algorithm with an optimal approximation ratio. Moreover, our algorithm only needs to access the agents' rankings on the edges. Finally, we extend our problem to submodular objective functions and Matroid rank constraints.

AAAI Conference 2022 Conference Paper

Learning-Augmented Algorithms for Online Steiner Tree

• Chenyang Xu
• Benjamin Moseley

This paper considers the recently popular beyond-worst-case algorithm analysis model which integrates machine-learned predictions with online algorithm design. We consider the online Steiner tree problem in this model for both directed and undirected graphs. Steiner tree is known to have strong lower bounds in the online setting and any algorithm’s worst-case guarantee is far from desirable. This paper considers algorithms that predict which terminal arrives online. The predictions may be incorrect and the algorithms’ performance is parameterized by the number of incorrectly predicted terminals. These guarantees ensure that algorithms break through the online lower bounds with good predictions and the competitive ratio gracefully degrades as the prediction error grows. We then observe that the theory is predictive of what will occur empirically. We show on graphs where terminals are drawn from a distribution, the new online algorithms have strong performance even with modestly correct predictions.

IJCAI Conference 2022 Conference Paper

Mechanism Design with Predictions

• Chenyang Xu
• Pinyan Lu

Improving algorithms via predictions is a very active research topic in recent years. This paper initiates the systematic study of mechanism design in this model. In a number of well-studied mechanism design settings, we make use of imperfect predictions to design mechanisms that perform much better than traditional mechanisms if the predictions are accurate (consistency), while always retaining worst-case guarantees even with very imprecise predictions (robustness). Furthermore, we refer to the largest prediction error sufficient to give a good performance as the error tolerance of a mechanism, and observe that an intrinsic tradeoff among consistency, robustness and error tolerance is common for mechanism design with predictions.

YNIMG Journal 2004 Journal Article

Cortical surface segmentation and mapping

• Duygu Tosun
• Maryam E. Rettmann
• Xiao Han
• Xiaodong Tao
• Chenyang Xu
• Susan M. Resnick
• Dzung L. Pham
• Jerry L. Prince

Segmentation and mapping of the human cerebral cortex from magnetic resonance (MR) images plays an important role in neuroscience and medicine. This paper describes a comprehensive approach for cortical reconstruction, flattening, and sulcal segmentation. Robustness to imaging artifacts and anatomical consistency are key achievements in an overall approach that is nearly fully automatic and computationally fast. Results demonstrating the application of this approach to a study of cortical thickness changes in aging are presented.

YNIMG Journal 2004 Journal Article

CRUISE: Cortical reconstruction using implicit surface evolution

• Xiao Han
• Dzung L. Pham
• Duygu Tosun
• Maryam E. Rettmann
• Chenyang Xu
• Jerry L. Prince

Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images play an important role in neuroscience and medicine. A successful segmentation method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. A method for the automatic reconstruction of the inner, central, and outer surfaces of the cerebral cortex from T1-weighted MR brain images is presented. The method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). The algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self-intersections. Validation results on real MR data are presented to demonstrate the performance of the method.

YNIMG Journal 2002 Journal Article

Automated Sulcal Segmentation Using Watersheds on the Cortical Surface

• Maryam E. Rettmann
• Xiao Han
• Chenyang Xu
• Jerry L. Prince

The human cortical surface is a highly complex, folded structure. Sulci, the spaces between the folds, define location on the cortex and provide a parcellation into anatomically distinct areas. A topic that has recently received increased attention is the segmentation of these sulci from magnetic resonance images, with most work focusing on extracting either the sulcal spaces between the folds or curve representations of sulci. Unlike these methods, we propose a technique that extracts actual regions of the cortical surface that surround sulci, which we call “sulcal regions. ” The method is based on a watershed algorithm applied to a geodesic depth measure on the cortical surface. A well-known problem with the watershed algorithm is a tendency toward oversegmentation, meaning that a single region is segmented as several pieces. To address this problem, we propose a postprocessing algorithm that merges appropriate segments from the watershed algorithm. The sulcal regions are then manually labeled by simply selecting the appropriate regions with a mouse click and a preliminary study of sulcal depth is reported. Finally, a scheme is presented for computing a complete parcellation of the cortical surface.

YNIMG Journal 2001 Journal Article

Calculation of human cerebral cortical thickness on opposing sulcalanks

• Duygu Tosun
• Maryam E. Rettmann
• Xiaodong Tao
• Xiao Han
• Chenyang Xu
• Jerry L. Prince

YNIMG Journal 2000 Journal Article

A new interactive surface editing tool for generating truth models of the human cortical surface

• Daphne N. Yu
• Chenyang Xu
• Jerry L. Prince

YNIMG Journal 2000 Journal Article

Improving cortical surface reconstruction accuracy usingan anatomatically consistent gray matter representation

• Chenyang Xu
• Xiao Han
• Jerry L. Prince

YNIMG Journal 1998 Journal Article

A Spherical Map for Cortical Geometry

• Chenyang Xu
• Maryam Etemad
• Daphne Yu
• Dzung L. Pham
• Jerry L. Prince

YNIMG Journal 1998 Journal Article

Reconstruction of the Human Cortical Surface from MR Images

• Chenyang Xu
• Dzung L. Pham
• Jerry L. Prince