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Yiwei Lou

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5 papers

1 author row

AAAI Conference 2026 Conference Paper

Beyond Conservation: Flexible Molecular Assembly with Unbalanced Diffusion Bridge

Rongchao Zhang
Yiwei Lou
Yu Huang
Yi Xin
Yongzhi Cao
Hanpin Wang

Molecular assembly (MA) has long been a fundamental task in chemistry and biology, with the potential to create new materials and enable novel functions beyond the molecular scale. However, its vast conformational search space poses substantial challenges, and current generative models remain limited in capturing molecular flexibility and preventing non-physical poses. In this paper, we propose AssemUDB, a diffusion bridge–based framework that learns transport mappings between two distinct flexible domains for molecular assembly generation. We reformulate the marginal matching constraint of diffusion bridges as a coupling distribution governed by unbalanced transport rather than imposing strict conservation. Subsequently, we employ a progressive process from structural relaxation in Euclidean space to assembly on the SE(3) manifold. This relaxation of marginal conservation grants the generative model greater flexibility and leads to more physically plausible atom placements. Comprehensive experiments demonstrate the superior performance of AssemUDB. Notably, we find that the method demonstrates performance comparable to, or even better than, mature tools such as PackMol for packing tasks.

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AAAI Conference 2026 Conference Paper

Multitasks-based Deep Evidential Fusion Network for Blind Image Quality Assessment

Yiwei Lou
Yuanpeng He
Rongchao Zhang
Yongzhi Cao
Hanpin Wang
Yu Huang

Blind image quality assessment (BIQA) methods often incorporate auxiliary tasks to improve performance. However, existing approaches face limitations due to insufficient integration and a lack of flexible uncertainty estimation, leading to suboptimal performance. To address these challenges, we propose a multitasks-based Deep Evidential Fusion Network (DEFNet) for BIQA, which performs multitask optimization with the assistance of scene and distortion type classification tasks. To achieve a more robust and reliable representation, we design a novel trustworthy information fusion strategy. It first combines diverse features and patterns across sub-regions to enhance information richness, and then performs local-global information fusion by balancing fine-grained details with coarse-grained context. Moreover, DEFNet exploits advanced uncertainty estimation technique inspired by evidential learning with the help of normal-inverse gamma distribution mixture. Extensive experiments on both synthetic and authentic distortion datasets demonstrate the effectiveness and robustness of the proposed framework. Additional evaluation and analysis are carried out to highlight its strong generalization capability and adaptability to previously unseen scenarios.

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AAAI Conference 2025 Conference Paper

Exploit Your Latents: Coarse-Grained Protein Backmapping with Latent Diffusion Models

Rongchao Zhang
Yu Huang
Yiwei Lou
Yi Xin
Haixu Chen
Yongzhi Cao
Hanpin Wang

Coarse-grained (CG) molecular dynamics of proteins is a preferred approach to studying large molecules on extended time scales by condensing the entire atomic model into a limited number of pseudo-atoms and preserving the thermodynamic properties of the system. However, the significantly increased efficiency impedes the analysis of substantial physicochemical information, since high-resolution atomic details are sacrificed to accelerate simulation. In this paper, we propose LatCPB, a generative approach based on diffusion that enables high-resolution backmapping of CG proteins. Specifically, our model encodes an all-atom into discrete latent embeddings, aligned with learnable multimodal discrete priors for circumventing posterior collapse and maintaining the discrete properties of the protein sequence. During the generation, we further design a latent diffusion process within the continuous latent space due to the potential stochastics in the data. Moreover, LatCPB performs a contrastive learning strategy in latent space to separate feature representations of various molecules and conformations of the same molecule, thus enhancing the comprehension of molecular representational diversity. Experimental results demonstrate that LatCPB is able to backmap CG proteins effectively and achieve outstanding performance.

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AAAI Conference 2024 Conference Paper

A Learnable Discrete-Prior Fusion Autoencoder with Contrastive Learning for Tabular Data Synthesis

Rongchao Zhang
Yiwei Lou
Dexuan Xu
Yongzhi Cao
Hanpin Wang
Yu Huang

The actual collection of tabular data for sharing involves confidentiality and privacy constraints, leaving the potential risks of machine learning for interventional data analysis unsafely averted. Synthetic data has emerged recently as a privacy-protecting solution to address this challenge. However, existing approaches regard discrete and continuous modal features as separate entities, thus falling short in properly capturing their inherent correlations. In this paper, we propose a novel contrastive learning guided Gaussian Transformer autoencoder, termed GTCoder, to synthesize photo-realistic multimodal tabular data for scientific research. Our approach introduces a transformer-based fusion module that seamlessly integrates multimodal features, permitting for mining more informative latent representations. The attention within the fusion module directs the integrated output features to focus on critical components that facilitate the task of generating latent embeddings. Moreover, we formulate a contrastive learning strategy to implicitly constrain the embeddings from discrete features in the latent feature space by encouraging the similar discrete feature distributions closer while pushing the dissimilar further away, in order to better enhance the representation of the latent embedding. Experimental results indicate that GTCoder is effective to generate photo-realistic synthetic data, with interactive interpretation of latent embedding, and performs favorably against some baselines on most real-world and simulated datasets.

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IJCAI Conference 2024 Conference Paper

Label Leakage in Vertical Federated Learning: A Survey

Yige Liu
Yiwei Lou
Yang Liu
Yongzhi Cao
Hanpin Wang

Vertical federated learning (VFL) is a distributed machine learning paradigm that collaboratively trains models using passive parties with features and an active party with additional labels. While VFL offers privacy preservation through data localization, the threat of label leakage remains a significant challenge. Label leakage occurs due to label inference attacks, where passive parties attempt to infer labels for their privacy and commercial value. Extensive research has been conducted on this specific VFL attack, but a comprehensive summary is still lacking. To bridge this gap, our paper aims to survey the existing label inference attacks and defenses. We propose two new taxonomies for both label inference attacks and defenses, respectively. Beyond summarizing the current state of research, we highlight techniques that we believe hold potential and could significantly influence future studies. Moreover, experimental benchmark datasets and evaluation metrics are summarized to provide a guideline for subsequent work.

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