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Jiayu Li

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4 papers

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AAAI Conference 2026 Conference Paper

Preference Optimization via Contrastive Divergence: Your Policy Is Secretly an NLL Estimator

Zhuotong Chen
Fang Liu
Xuan Zhu
Haozhu Wang
Jiayu Li
Yanjun Qi
Mohammad Ghavamzadeh

Existing studies on preference optimization (PO) have been focused on constructing pairwise preference data following simple heuristics, such as maximizing the margin between chosen and rejected responses based on human (or AI) ratings. In this work, we develop a novel PO framework that provides theoretical guidance to effectively sample rejected responses. To achieve this, we formulate PO as minimizing the negative log-likelihood (NLL) of a probability model and propose a sampling-based solution to estimate its normalization constant via contrastive divergence. We show that these estimative samples can act as rejected responses in PO. Leveraging the connection established between PO and NLL estimation, we propose a novel PO algorithm, called Monte-Carlo-based PO (MC-PO), that applies a MC kernel to sample *hard negatives* w.r.t.~the log-likelihood of the target policy. Intuitively, these hard negatives represent the rejected samples that are most difficult for the current policy to differentiate. We show that MC-PO outperforms existing SOTA baselines on popular alignment benchmarks.

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AAAI Conference 2025 Conference Paper

VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis

Chao Pang
Xingxing Weng
Jiang Wu
Jiayu Li
Yi Liu
Jiaxing Sun
Weijia Li
Shuai Wang

This paper develops a Versatile and Honest vision language Model (VHM) for remote sensing image analysis. VHM is built on a large-scale remote sensing image-text dataset with rich-content captions (VersaD), and an honest instruction dataset comprising both factual and deceptive questions (HnstD). Unlike prevailing remote sensing image-text datasets, in which image captions focus on a few prominent objects and their relationships, VersaD captions provide detailed information about image properties, object attributes, and the overall scene. This comprehensive captioning enables VHM to thoroughly understand remote sensing images and perform diverse remote sensing tasks. Moreover, different from existing remote sensing instruction datasets that only include factual questions, HnstD contains additional deceptive questions stemming from the non-existence of objects. This feature prevents VHM from producing affirmative answers to nonsense queries, thereby ensuring its honesty. In our experiments, VHM significantly outperforms various vision language models on common tasks of scene classification, visual question answering, and visual grounding. Additionally, VHM achieves competent performance on several unexplored tasks, such as building vectorizing, multi-label classification and honest question answering.

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YNICL Journal 2024 Journal Article

Unveiling MRI markers for Parkinson’s Disease: GABAergic dysfunction and cortical changes

Yuan Tian
Sijia Geng
Tianyi Liu
Qi Wang
Jianxiu Lian
Liangjie Lin
Jiayu Li
Tao Gong

OBJECTIVE: The study aimed to investigate changes in basal levels of the inhibitory γ-aminobutyric acid (GABA) neurotransmitter in the sensorimotor cortex (SMC) and cortical gyrification in patients with Parkinson's disease (PD), which could further identify potential imaging biomarkers for PD, particularly in patients with early-onset Parkinson's disease (EOPD). METHOD: Fifty patients with PD (EOPD: 10, late-onset Parkinson's disease [LOPD]: 40) and fifty-two age- and gender-matched healthy controls (HC) underwent GABA-edited 1H MRS of the SMC and high-resolution 3D T1-weighted brain imaging. GABA levels and local gyrification index (LGI) were calculated to assess GABAergic and cortical gyrification deficits in PD. RESULT: The Pearson correlation coefficients revealed significant negative associations between eight indicators, including GABA/Cr level and local gyrification index (LGI) of specific cortical regions (precentral, postcentral, entorhinal, superiortemporal, posteriorcingulate, cuneus, and transversetemporal cortex), and the likelihood of Parkinson's disease (r < -0.4, p < 0.001). Additionally, GABA levels were significantly lower in the SMC region of both EOPD and LOPD patients compared to healthy controls (mean ± SD [u.i.]: EOPD=0.081 ± 0.022 vs. Young-HC=0.112 ± 0.021, p = 0.003; LOPD=0.054 ± 0.024 vs. Old-HC=0.099 ± 0.021, p < 0.001). The logistic regression model was established by using multivariate analysis, identifying two statistically significant indicators: GABA/Cr and LGI of the transversetemporal. The combined model exhibited the highest AUC values in both younger and older populations. CONCLUSION: GABAergic dysfunction may play an important role in the pathogenesis of PD patients. Changes in neurotransmitter and morphological may serve as potential markers for the preclinical diagnosis and progression of PD, including EOPD.

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AAAI Conference 2019 Conference Paper

Universal Approximation Property and Equivalence of Stochastic Computing-Based Neural Networks and Binary Neural Networks

Yanzhi Wang
Zheng Zhan
Liang Zhao
Jian Tang
Siyue Wang
Jiayu Li
Bo Yuan
Wujie Wen

Large-scale deep neural networks are both memory and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated. Specific forms of binary neural networks (BNNs) and stochastic computing-based neural networks (SCNNs) are particularly appealing to hardware implementations since they can be implemented almost entirely with binary operations. Despite the obvious advantages in hardware implementation, these approximate computing techniques are questioned by researchers in terms of accuracy and universal applicability. Also it is important to understand the relative pros and cons of SCNNs and BNNs in theory and in actual hardware implementations. In order to address these concerns, in this paper we prove that the ”ideal” SCNNs and BNNs satisfy the universal approximation property with probability 1 (due to the stochastic behavior), which is a new angle from the original approximation property. The proof is conducted by first proving the property for SCNNs from the strong law of large numbers, and then using SCNNs as a “bridge” to prove for BNNs. Besides the universal approximation property, we also derive an appropriate bound for bit length M in order to provide insights for the actual neural network implementations. Based on the universal approximation property, we further prove that SCNNs and BNNs exhibit the same energy complexity. In other words, they have the same asymptotic energy consumption with the growth of network size. We also provide a detailed analysis of the pros and cons of SCNNs and BNNs for hardware implementations and conclude that SC- NNs are more suitable.

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