Author name cluster

Yuanbin Wu

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6 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Protein Design with Dynamic Protein Vocabulary

Nuowei Liu
Jiahao Kuang
Yanting Liu
Tao Ji
Changzhi Sun
Man Lan
Yuanbin Wu

Protein design is a fundamental challenge in biotechnology, aiming to design novel sequences with specific functions within the vast space of possible proteins. Recent advances in deep generative models have enabled function-based protein design from textual descriptions, yet struggle with structural plausibility. Inspired by classical protein design methods that leverage natural protein structures, we explore whether incorporating fragments from natural proteins can enhance foldability in generative models. Our empirical results show that even random incorporation of fragments improves foldability. Building on this insight, we introduce ProDVa, a novel protein design approach that integrates a text encoder for functional descriptions, a protein language model for designing proteins, and a fragment encoder to dynamically retrieve protein fragments based on textual functional descriptions. Experimental results demonstrate that our approach effectively designs protein sequences that are both functionally aligned and structurally plausible. Compared to state-of-the-art models, ProDVa achieves comparable function alignment using less than 0. 04% of the training data, while designing significantly more well-folded proteins, with the proportion of proteins having pLDDT above 70 increasing by 7. 38% and those with PAE below 10 increasing by 9. 62%.

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AAAI Conference 2021 Conference Paper

Generating CCG Categories

Yufang Liu
Tao Ji
Yuanbin Wu
Man Lan

Previous CCG supertaggers usually predict categories using multi-class classification. Despite their simplicity, internal structures of categories are usually ignored. The rich semantics inside these structures may help us to better handle relations among categories and bring more robustness into existing supertaggers. In this work, we propose to generate categories rather than classify them: each category is decomposed into a sequence of smaller atomic tags, and the tagger aims to generate the correct sequence. We show that with this finer view on categories, annotations of different categories could be shared and interactions with sentence contexts could be enhanced. The proposed category generator is able to achieve state-of-the-art tagging (95. 5% accuracy) and parsing (89. 8% labeled F1) performances on the standard CCGBank. Furthermore, its performances on infrequent (even unseen) categories, out-of-domain texts and low resource language give promising results on introducing generation models to the general CCG analyses.

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AAAI Conference 2019 Conference Paper

Distantly Supervised Entity Relation Extraction with Adapted Manual Annotations

Changzhi Sun
Yuanbin Wu

We investigate the task of distantly supervised joint entity relation extraction. It’s known that training with distant supervision will suffer from noisy samples. To tackle the problem, we propose to adapt a small manually labelled dataset to the large automatically generated dataset. By developing a novel adaptation algorithm, we are able to transfer the high quality but heterogeneous entity relation annotations in a robust and consistent way. Experiments on the benchmark NYT dataset show that our approach significantly outperforms state-ofthe-art methods.

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AAAI Conference 2018 Conference Paper

Inference on Syntactic and Semantic Structures for Machine Comprehension

Chenrui Li
Yuanbin Wu
Man Lan

Hidden variable models are important tools for solving open domain machine comprehension tasks and have achieved remarkable accuracy in many question answering benchmark datasets. Existing models impose strong independence assumptions on hidden variables, which leaves the interaction among them unexplored. Here we introduce linguistic structures to help capturing global evidence in hidden variable modeling. In the proposed algorithms, question-answer pairs are scored based on structured inference results on parse trees and semantic frames, which aims to assign hidden variables in a global optimal way. Experiments on the MCTest dataset demonstrate that the proposed models are highly competitive with state-of-the-art machine comprehension systems.

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NeurIPS Conference 2017 Conference Paper

A Learning Error Analysis for Structured Prediction with Approximate Inference

Yuanbin Wu
Man Lan
Shiliang Sun
Qi Zhang
Xuanjing Huang

In this work, we try to understand the differences between exact and approximate inference algorithms in structured prediction. We compare the estimation and approximation error of both underestimate and overestimate models. The result shows that, from the perspective of learning errors, performances of approximate inference could be as good as exact inference. The error analyses also suggest a new margin for existing learning algorithms. Empirical evaluations on text classification, sequential labelling and dependency parsing witness the success of approximate inference and the benefit of the proposed margin.

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ICML Conference 2015 Conference Paper

An Online Learning Algorithm for Bilinear Models

Yuanbin Wu
Shiliang Sun

We investigate the bilinear model, which is a matrix form linear model with the rank 1 constraint. A new online learning algorithm is proposed to train the model parameters. Our algorithm runs in the manner of online mirror descent, and gradients are computed by the power iteration. To analyze it, we give a new second order approximation of the squared spectral norm, which helps us to get a regret bound. Experiments on two sequential labelling tasks give positive results.

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