An inexact subsampled proximal Newton-type method for large-scale machine learning. - Xuanqing Liu; Cho-Jui Hsieh; Jason D. Lee; Yuekai Sun (CoRR, 2017)

Towards Robust Neural Networks via Random Self-ensemble. - Xuanqing Liu; Minhao Cheng; Huan Zhang 001; Cho-Jui Hsieh (ECCV (7), 2018)

Fast Variance Reduction Method with Stochastic Batch Size. - Xuanqing Liu; Cho-Jui Hsieh (ICML, 2018)

From Adversarial Training to Generative Adversarial Networks. - Xuanqing Liu; Cho-Jui Hsieh (CoRR, 2018)

Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient. - Liu Liu 014; Xuanqing Liu; Cho-Jui Hsieh; Dacheng Tao (CoRR, 2018)

Rob-GAN: Generator, Discriminator, and Adversarial Attacker. - Xuanqing Liu; Cho-Jui Hsieh (CVPR, 2019)

Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network. - Xuanqing Liu; Yao Li 015; Chongruo Wu; Cho-Jui Hsieh (ICLR (Poster), 2019)

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. - Wei-Lin Chiang; Xuanqing Liu; Si Si; Yang Li 058; Samy Bengio; Cho-Jui Hsieh (KDD, 2019)

A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. - Xuanqing Liu; Si Si; Jerry Zhu; Yang Li 058; Cho-Jui Hsieh (NeurIPS, 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)

Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective. - Lu Wang 031; Xuanqing Liu; Jinfeng Yi; Zhi-Hua Zhou; Cho-Jui Hsieh (CoRR, 2019)

A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. - Xuanqing Liu; Si Si; Xiaojin Zhu 001; Yang Li 058; Cho-Jui Hsieh (CoRR, 2019)

GraphDefense: Towards Robust Graph Convolutional Networks. - Xiaoyun Wang 001; Xuanqing Liu; Cho-Jui Hsieh (CoRR, 2019)

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 (CVPR, 2020)

Learning to Encode Position for Transformer with Continuous Dynamical Model. - Xuanqing Liu; Hsiang-Fu Yu; Inderjit S. Dhillon; Cho-Jui Hsieh (ICML, 2020)

Provably Robust Metric Learning. - Lu Wang 031; Xuanqing Liu; Jinfeng Yi; Yuan Jiang 001; Cho-Jui Hsieh (NeurIPS, 2020)

Gradient Boosting Neural Networks: GrowNet. - Sarkhan Badirli; Xuanqing Liu; Zhengming Xing; Avradeep Bhowmik; Sathiya S. Keerthi (CoRR, 2020)

Improving the Speed and Quality of GAN by Adversarial Training. - Jiachen Zhong; Xuanqing Liu; Cho-Jui Hsieh (CoRR, 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 001; Si Si; Cho-Jui Hsieh (CoRR, 2020)

Evaluations and Methods for Explanation through Robustness Analysis. - Cheng-Yu Hsieh; Chih-Kuan Yeh; Xuanqing Liu; Pradeep Kumar Ravikumar; Seungyeon Kim 001; Sanjiv Kumar; Cho-Jui Hsieh (ICLR, 2021)

Label Disentanglement in Partition-based Extreme Multilabel Classification. - Xuanqing Liu; Wei-Cheng Chang; Hsiang-Fu Yu; Cho-Jui Hsieh; Inderjit S. Dhillon (NeurIPS, 2021)

Building Trustworthy Machine Learning Models. - Xuanqing Liu (2021)

Stochastic Optimization for Non-convex Problem with Inexact Hessian Matrix, Gradient, and Function. - Liu Liu 014; Xuanqing Liu; Cho-Jui Hsieh; Dacheng Tao (CoRR, 2023)

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 (EMNLP (Industry Track), 2024)

GRAM: Generative Retrieval Augmented Matching of Data Schemas in the Context of Data Security. - Xuanqing Liu; Runhui Wang; Yang Song; Luyang Kong (KDD, 2024)

Solar Flare-Induced Prenoon-Postnoon Asymmetry of Ionospheric Electron Density. - Xuanqing Liu; Jing Liu 073 (Zenodo, 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)

Data of TIEGCM Simulation for manuscript "On the Relationship between Traveling Atmospheric Disturbances and Traveling Ionospheric Disturbances". - Bowei Liu; Xuanqing Liu; Jing Liu 073 (Zenodo, 2024)

Data of GOES X-ray flux, GRACE neutral density, and TIEGCM Simulation for manuscript "On the Relationship between Traveling Atmospheric Disturbances and Traveling Ionospheric Disturbances". - Bowei Liu; Xuanqing Liu; Jing Liu 073 (Zenodo, 2024)

Learning LLM Preference over Intra-Dialogue Pairs: A Framework for Utterance-level Understandings. - Xuanqing Liu; Luyang Kong; Wei Niu; Afshin Khashei; Belinda Zeng; Steve Johnson; Jon Jay; Davor Golac; Matt Pope (NAACL (Industry Track), 2025)

Stochastic Optimization for Nonconvex Problem With Inexact Hessian Matrix, Gradient, and Function. - Liu Liu 014; Xuanqing Liu; Cho-Jui Hsieh; Dacheng Tao (IEEE Trans. Neural Networks Learn. Syst., 2025)

Declarative Data Pipeline for Large Scale ML Services. - Yunzhao Yang; Runhui Wang; Xuanqing Liu; Adit Krishnan; Yefan Tao; Yuqian Deng; Kuangyou Yao; Peiyuan Sun; Henrik Johnson; Aditi sinha; Davor Golac; Gerald Friedland; et al. (CoRR, 2025)