Search Results for author: Binghui Xie

Found 7 papers, 3 papers with code

Enhancing Neural Subset Selection: Integrating Background Information into Set Representations

no code implementations5 Feb 2024 Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao, Bo Han, Wei Meng, James Cheng

Learning neural subset selection tasks, such as compound selection in AI-aided drug discovery, have become increasingly pivotal across diverse applications.

Drug Discovery

Enhancing Evolving Domain Generalization through Dynamic Latent Representations

no code implementations16 Jan 2024 Binghui Xie, Yongqiang Chen, Jiaqi Wang, Kaiwen Zhou, Bo Han, Wei Meng, James Cheng

However, in non-stationary tasks where new domains evolve in an underlying continuous structure, such as time, merely extracting the invariant features is insufficient for generalization to the evolving new domains.

Evolving Domain Generalization

Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes

no code implementations30 Nov 2023 Yongqiang Chen, Binghui Xie, Kaiwen Zhou, Bo Han, Yatao Bian, James Cheng

Surprisingly, DeepSet outperforms transformers across a variety of distribution shifts, implying that preserving permutation invariance symmetry to input demonstrations is crucial for OOD ICL.

In-Context Learning

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack

1 code implementation15 Jun 2022 Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng

The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available.

Adversarial Robustness Computational Efficiency

An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms

no code implementations28 Apr 2022 Binghui Xie, Chenhan Jin, Kaiwen Zhou, James Cheng, Wei Meng

Stochastic variance reduced methods have shown strong performance in solving finite-sum problems.

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

3 code implementations11 Feb 2022 Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

Despite recent success in using the invariance principle for out-of-distribution (OOD) generalization on Euclidean data (e. g., images), studies on graph data are still limited.

Drug Discovery Graph Learning +1

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