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Jiahao Yang

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

Expert-Inspired Multi-Agent Coordination for Multi-Objective Molecular Optimization

Daojian Zeng
Tianle Li
Jiahao Yang
Jiacai Yi
Xieping Gao
Lincheng Jiang
Tengfei Ma
Xiangxiang Zeng

Multi-objective molecular optimization is a fundamental yet inherently challenging task in drug discovery, as it requires simultaneously optimizing multiple, often conflicting, molecular properties. Although recent deep learning methods have shown promise, they often lack objective-specific specialization and dynamic coordination, making them ineffective in handling competing objectives and difficult to scale in complex, high-dimensional molecular design tasks. Inspired by the division of labor among domain experts in medicinal chemistry, we propose MAMO, a multi-agent framework for molecular design that simulates expert collaboration. Each agent specializes in optimizing a single objective, and their interactions are orchestrated by a central scheduling module that dynamically reallocates tasks based on evaluation feedback. This coordination mechanism enables interpretable and goal-conditioned optimization while adaptively balancing conflicting objectives. Extensive experiments on benchmark datasets demonstrate that MAMO consistently achieves superior performance in both objective quality and Pareto diversity, particularly in scenarios with strong inter-objective conflict. Our results highlight the potential of multi-agent coordination strategies for scalable and conflict-aware molecular design.

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AAAI Conference 2025 Conference Paper

Multi-Objective Molecular Design Through Learning Latent Pareto Set

Yiping Liu
Jiahao Yang
Xuanbai Ren
Zhang Xinyi
Yuansheng Liu
Bosheng Song
Xiangxiang Zeng
Hisao Ishibuchi

Molecular design inherently involves the optimization of multiple conflicting objectives, such as enhancing bio-activity and ensuring synthesizability. Evaluating these objectives often requires resource-intensive computations or physical experiments. Current molecular design methodologies typically approximate the Pareto set using a limited number of molecules. In this paper, we present an innovative approach, called Multi-Objective Molecular Design through Learning Latent Pareto Set (MLPS). MLPS initially utilizes an encoder-decoder model to seamlessly transform the discrete chemical space into a continuous latent space. We then employ local Bayesian optimization models to efficiently search for local optimal solutions (i.e., molecules) within predefined trust regions. Using surrogate objective values derived from these local models, we train a global Pareto set learning model to understand the mapping between direction vectors (called “preferences”) in the objective space and the entire Pareto set in the continuous latent space. Both the global Pareto set learning model and local Bayesian optimization models collaborate to discover high-quality solutions and adapt the trust regions dynamically. Our work is an effective endeavor towards learning the Pareto set for multi-objective molecular design, providing decision-makers with the capability to fine-tune their preferences and thoroughly explore the Pareto set. Experimental results demonstrate that MLPS achieves state-of-the-art performance across various multi-objective scenarios, encompassing diverse objective types and varying numbers of objectives. The effectiveness of MLPS was further validated through real-world challenges in discovering antifungal peptides with low toxicity and high activity.

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NeurIPS Conference 2025 Conference Paper

Physics-Driven Spatiotemporal Modeling for AI-Generated Video Detection

Shuhai Zhang
ZiHao Lian
Jiahao Yang
Daiyuan Li
Guoxuan Pang
Feng Liu
Bo Han
Shutao Li

AI-generated videos have achieved near-perfect visual realism (e. g. , Sora), urgently necessitating reliable detection mechanisms. However, detecting such videos faces significant challenges in modeling high-dimensional spatiotemporal dynamics and identifying subtle anomalies that violate physical laws. In this paper, we propose a physics-driven AI-generated video detection paradigm based on probability flow conservation principles. Specifically, we propose a statistic called Normalized Spatiotemporal Gradient (NSG), which quantifies the ratio of spatial probability gradients to temporal density changes, explicitly capturing deviations from natural video dynamics. Leveraging pre-trained diffusion models, we develop an NSG estimator through spatial gradients approximation and motion-aware temporal modeling without complex motion decomposition while preserving physical constraints. Building on this, we propose an NSG-based video detection method (NSG-VD) that computes the Maximum Mean Discrepancy (MMD) between NSG features of the test and real videos as a detection metric. Last, we derive an upper bound of NSG feature distances between real and generated videos, proving that generated videos exhibit amplified discrepancies due to distributional shifts. Extensive experiments confirm that NSG-VD outperforms state-of-the-art baselines by 16. 00\% in Recall and 10. 75\% in F1-Score, validating the superior performance of NSG-VD. The source code is available at \url{https: //github. com/ZSHsh98/NSG-VD}.

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EAAI Journal 2024 Journal Article

A hierarchical deep model integrating economic facts for stock movement prediction

Jiahao Yang
Ming Zhang
Shuo Feng
Xuejun Zhang
Xing Bai

Accurate stock movement prediction is essential to profit from the stock market. However, this task is challenging due to the complexity and non-stationary nature of the market. Deep learning methods have obtained more attention and success in mining price movement patterns. However, some limitations affect their performances. In general, the stock market is ever-changing, and many factors affect stock movement, so capturing the stock movement patterns is hard without enough prior information. To tackle it, we consider employing economic facts to help improve the deep learning method. In this paper, we propose a novel Hierarchical Deep learning Model that fuses Economic Facts (HDMEF) to predict stock movement from the micro to the macro tiers: the individual, industry, and whole market tiers. Specifically, we present three well-designed modules to separately model them based on the Capital Asset Pricing Model (CAPM), the herding effects, and the holiday effects in the stock market. Experiments on the A-share CSI300 and CSI500 indexes demonstrate that our proposed method performs best on all test phases compared with previous competitive baselines, even an absolute improvement of 2%–3% on some test phases where all the baselines act poor, proving our method is more efficient and robust in different market conditions. In addition, we do an ablation study to analyze the role of various economic effects used in our model, and the results prove that each module is helpful for prediction.

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ICLR Conference 2024 Conference Paper

Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy

Shuhai Zhang
Yiliao Song
Jiahao Yang
Yuanqing Li 0001
Bo Han 0003
Mingkui Tan

Large language models (LLMs) such as ChatGPT have exhibited remarkable performance in generating human-like texts. However, machine-generated texts (MGTs) may carry critical risks, such as plagiarism issues and hallucination information. Therefore, it is very urgent and important to detect MGTs in many situations. Unfortunately, it is challenging to distinguish MGTs and human-written texts because the distributional discrepancy between them is often very subtle due to the remarkable performance of LLMS. In this paper, we seek to exploit \textit{maximum mean discrepancy} (MMD) to address this issue in the sense that MMD can well identify distributional discrepancies. However, directly training a detector with MMD using diverse MGTs will incur a significantly increased variance of MMD since MGTs may contain \textit{multiple text populations} due to various LLMs. This will severely impair MMD's ability to measure the difference between two samples. To tackle this, we propose a novel \textit{multi-population} aware optimization method for MMD called MMD-MP, which can \textit{avoid variance increases} and thus improve the stability to measure the distributional discrepancy. Relying on MMD-MP, we develop two methods for paragraph-based and sentence-based detection, respectively. Extensive experiments on various LLMs, \eg, GPT2 and ChatGPT, show superior detection performance of our MMD-MP.

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ICML Conference 2023 Conference Paper

Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score

Shuhai Zhang
Feng Liu 0003
Jiahao Yang
Yifan Yang
Changsheng Li
Bo Han 0003
Mingkui Tan

Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimating or comparing two data distributions is extremely difficult, especially in high-dimension spaces. Recently, the gradient of log probability density (a. k. a. , score) w. r. t. the sample is used as an alternative statistic to compute. However, we find that the score is sensitive in identifying adversarial samples due to insufficient information with one sample only. In this paper, we propose a new statistic called expected perturbation score (EPS), which is essentially the expected score of a sample after various perturbations. Specifically, to obtain adequate information regarding one sample, we perturb it by adding various noises to capture its multi-view observations. We theoretically prove that EPS is a proper statistic to compute the discrepancy between two samples under mild conditions. In practice, we can use a pre-trained diffusion model to estimate EPS for each sample. Last, we pro- pose an EPS-based adversarial detection (EPS- AD) method, in which we develop EPS-based maximum mean discrepancy (MMD) as a metric to measure the discrepancy between the test sample and natural samples. We also prove that the EPS-based MMD between natural and adversarial samples is larger than that among natural samples. Extensive experiments show the superior adversarial detection performance of our EPS-AD.

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IJCAI Conference 2021 Conference Paper

What If We Could Not See? Counterfactual Analysis for Egocentric Action Anticipation

Tianyu Zhang
Weiqing Min
Jiahao Yang
Tao Liu
Shuqiang Jiang
Yong Rui

Egocentric action anticipation aims at predicting the near future based on past observation in first-person vision. While future actions may be wrongly predicted due to the dataset bias, we present a counterfactual analysis framework for egocentric action anticipation (CA-EAA) to enhance the capacity. In the factual case, we can predict the upcoming action based on visual features and semantic labels from past observation. Imagining one counterfactual situation where no visual representation had been observed, we would obtain a counterfactual predicted action only using past semantic labels. In this way, we can reduce the side-effect caused by semantic labels via a comparison between factual and counterfactual outcomes, which moves a step towards unbiased prediction for egocentric action anticipation. We conduct experiments on two large-scale egocentric video datasets. Qualitative and quantitative results validate the effectiveness of our proposed CA-EAA.

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