Xuanqing Liu

According to our database, Xuanqing Liu authored at least 26 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

GRAM: Generative Retrieval Augmented Matching of Data Schemas in the Context of Data Security.

Xuanqing Liu, Luyang Kong, Runhui Wang, Patrick Song, Austin Nevins, Henrik Johnson, Nimish Amlathe, Davor Golac

CoRR, 2024

GRAM: Generative Retrieval Augmented Matching of Data Schemas in the Context of Data Security.

Xuanqing Liu, Runhui Wang, Yang Song, Luyang Kong

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

BPID: A Benchmark for Personal Identity Deduplication.

Runhui Wang, Yefan Tao, Adit Krishnan, Luyang Kong, Xuanqing Liu, Yuqian Deng, Yunzhao Yang, Henrik Johnson, Andrew Borthwick, Shobhit Gupta, Aditi Gundlapalli, Davor Golac

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: EMNLP 2024, 2024

2023

Stochastic Optimization for Non-convex Problem with Inexact Hessian Matrix, Gradient, and Function.

Liu Liu, Xuanqing Liu, Cho-Jui Hsieh, Dacheng Tao

CoRR, 2023

2021

Building Trustworthy Machine Learning Models.

Xuanqing Liu

PhD thesis, 2021

Label Disentanglement in Partition-based Extreme Multilabel Classification.

Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Evaluations and Methods for Explanation through Robustness Analysis.

Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers.

Yuanhao Xiong, Xuanqing Liu, Li-Cheng Lan, Yang You, Si Si, Cho-Jui Hsieh

CoRR, 2020

Improving the Speed and Quality of GAN by Adversarial Training.

Jiachen Zhong, Xuanqing Liu, Cho-Jui Hsieh

CoRR, 2020

Gradient Boosting Neural Networks: GrowNet.

Sarkhan Badirli, Xuanqing Liu, Zhengming Xing, Avradeep Bhowmik, Sathiya S. Keerthi

CoRR, 2020

Provably Robust Metric Learning.

Lu Wang, Xuanqing Liu, Jinfeng Yi, Yuan Jiang, Cho-Jui Hsieh

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning to Encode Position for Transformer with Continuous Dynamical Model.

Xuanqing Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh

Proceedings of the 37th International Conference on Machine Learning, 2020

How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework.

Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

GraphDefense: Towards Robust Graph Convolutional Networks.

Xiaoyun Wang, Xuanqing Liu, Cho-Jui Hsieh

CoRR, 2019

A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning.

Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, Cho-Jui Hsieh

CoRR, 2019

Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective.

Lu Wang, Xuanqing Liu, Jinfeng Yi, Zhi-Hua Zhou, Cho-Jui Hsieh

CoRR, 2019

Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise.

Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh

CoRR, 2019

A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning.

Xuanqing Liu, Si Si, Jerry Zhu, Yang Li, Cho-Jui Hsieh

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks.

Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network.

Xuanqing Liu, Yao Li, Chongruo Wu, Cho-Jui Hsieh

Proceedings of the 7th International Conference on Learning Representations, 2019

Rob-GAN: Generator, Discriminator, and Adversarial Attacker.

Xuanqing Liu, Cho-Jui Hsieh

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient.

Liu Liu, Xuanqing Liu, Cho-Jui Hsieh, Dacheng Tao

CoRR, 2018

From Adversarial Training to Generative Adversarial Networks.

Xuanqing Liu, Cho-Jui Hsieh

CoRR, 2018

Fast Variance Reduction Method with Stochastic Batch Size.

Xuanqing Liu, Cho-Jui Hsieh

Proceedings of the 35th International Conference on Machine Learning, 2018

Towards Robust Neural Networks via Random Self-ensemble.

Xuanqing Liu, Minhao Cheng, Huan Zhang, Cho-Jui Hsieh

Proceedings of the Computer Vision - ECCV 2018, 2018

2017

An inexact subsampled proximal Newton-type method for large-scale machine learning.

Xuanqing Liu, Cho-Jui Hsieh, Jason D. Lee, Yuekai Sun

CoRR, 2017