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Xixian Chen

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5 papers

2 author rows

IJCAI Conference 2025 Conference Paper

Generative Co-Design of Antibody Sequences and Structures via Black-Box Guidance in a Shared Latent Space

Yinghua Yao
Yuangang Pan
Xixian Chen

Advancements in deep generative models have enabled the joint modeling of antibody sequence and structure, given the antigen-antibody complex as context. However, existing approaches for optimizing complementarity-determining regions (CDRs) to improve developability properties operate in the raw data space, leading to excessively costly evaluations due to the inefficient search process. To address this, we propose LatEnt blAck-box Design (LEAD), a sequence-structure co-design framework that optimizes both sequence and structure within their shared latent space. Optimizing shared latent codes can not only break through the limitations of existing methods, but also ensure synchronization of different modality designs. Particularly, we design a black-box guidance strategy to accommodate real-world scenarios where many property evaluators are non-differentiable. Experimental results demonstrate that our LEAD achieves superior optimization performance for both single and multi-property objectives. Notably, LEAD reduces query consumption by a half while surpassing baseline methods in property optimization. The code is available at https: //github. com/EvaFlower/LatEnt-blAck-box-Design.

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IJCAI Conference 2019 Conference Paper

Parallel Wasserstein Generative Adversarial Nets with Multiple Discriminators

Yuxin Su
Shenglin Zhao
Xixian Chen
Irwin King
Michael Lyu

Wasserstein Generative Adversarial Nets~(GANs) are newly proposed GAN algorithms and widely used in computer vision, web mining, information retrieval, etc. However, the existing algorithms with approximated Wasserstein loss converge slowly due to heavy computation cost and usually generate unstable results as well. In this paper, we solve the computation cost problem by speeding up the Wasserstein GANs from a well-designed communication efficient parallel architecture. Specifically, we develop a new problem formulation targeting the accurate evaluation of Wasserstein distance and propose an easily parallel optimization algorithm to train the Wasserstein GANs. Compared to traditional parallel architecture, our proposed framework is designed explicitly for the skew parameter updates between the generator network and discriminator network. Rigorous experiments reveal that our proposed framework achieves a significant improvement regarding convergence speed with comparable stability on generating images, compared to the state-of-the-art of Wasserstein GANs algorithms.

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UAI Conference 2017 Conference Paper

FROSH: FasteR Online Sketching Hashing

Xixian Chen
Irwin King
Michael R. Lyu

Many hashing methods, especially those that are in the data-dependent category with good learning accuracy, are still inefficient when dealing with three critical problems in modern data analysis. First, data usually come in a streaming fashion, but most of the existing hashing methods are batch-based models. Second, when data become huge, the extensive computational time, large space requirement, and multiple passes to load the data into memory will be prohibitive. Third, data often lack sufficient label information. Although the recently proposed Online Sketching Hashing (OSH) is promising to alleviate all three issues mentioned above, its training procedure still suffers from a high time complexity. In this paper, we propose a FasteR Online Sketching Hashing (FROSH) method to make the training process faster. Compared with OSH, we leverage fast transform to sketch data more compactly. Particularly, we derive independent transformations to guarantee the sketching accuracy, and design a novel implementation to make such transformations applicable to online data sketching without increasing the space cost. We rigorously prove that our method can yield a comparable learning accuracy with a lower time complexity and an equal space cost compared with OSH. Finally, extensive experiments on synthetic and realworld datasets demonstrate the excellent performance of our method.

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ICML Conference 2017 Conference Paper

Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data

Xixian Chen
Michael R. Lyu
Irwin King

Estimating covariance matrices is a fundamental technique in various domains, most notably in machine learning and signal processing. To tackle the challenges of extensive communication costs, large storage capacity requirements, and high processing time complexity when handling massive high-dimensional and distributed data, we propose an efficient and accurate covariance matrix estimation method via data compression. In contrast to previous data-oblivious compression schemes, we leverage a data-aware weighted sampling method to construct low-dimensional data for such estimation. We rigorously prove that our proposed estimator is unbiased and requires smaller data to achieve the same accuracy with specially designed sampling distributions. Besides, we depict that the computational procedures in our algorithm are efficient. All achievements imply an improved tradeoff between the estimation accuracy and computational costs. Finally, the extensive experiments on synthetic and real-world datasets validate the superior property of our method and illustrate that it significantly outperforms the state-of-the-art algorithms.

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IJCAI Conference 2015 Conference Paper

Training-Efficient Feature Map for Shift-Invariant Kernels

Xixian Chen
Haiqin Yang
Irwin King
Michael R. Lyu

Random feature map is popularly used to scale up kernel methods. However, employing a large number of mapped features to ensure an accurate approximation will still make the training time consuming. In this paper, we aim to improve the training efficiency of shift-invariant kernels by using fewer informative features without sacrificing precision. We propose a novel feature map method by extending Random Kitchen Sinks through fast datadependent subspace embedding to generate the desired features. More specifically, we describe two algorithms with different tradeoffs on the running speed and accuracy, and prove that O(l) features induced by them are able to perform as accurately as O(l2 ) features by other feature map methods. In addition, several experiments are conducted on the real-world datasets demonstrating the superiority of our proposed algorithms.

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