Xu, Zhiqiang

37 publications

AAAI 2025 A Gaussian Filter-Based 3D Registration Method for Series Section Electron Microscopy Zhenbang Zhang, Hongjia Li, Zhiqiang Xu, Wenjia Meng, Renmin Han

ICLRW 2025 Do Large Language Models Perceive Orderly Number Concepts as Humans? Xuanjie Liu, Cong Zeng, Shengkun Tang, Ziyu Wang, Zhiqiang Xu, Gus Xia

CPAL 2025 Exact and Rich Feature Learning Dynamics of Two-Layer Linear Networks Wei Huang, Wuyang Chen, Zhiqiang Xu, Zhangyang Wang, Taiji Suzuki

ICCV 2025 Golden Noise for Diffusion Models: A Learning Framework Zikai Zhou, Shitong Shao, Lichen Bai, Shufei Zhang, Zhiqiang Xu, Bo Han, Zeke Xie

NeurIPS 2025 Human Texts Are Outliers: Detecting LLM-Generated Texts via Out-of-Distribution Detection Cong Zeng, Shengkun Tang, Yuanzhou Chen, Zhiqiang Shen, Wenchao Yu, Xujiang Zhao, Haifeng Chen, Wei Cheng, Zhiqiang Xu

ICLR 2025 Measuring and Improving Engagement of Text-to-Image Generation Models Varun Khurana, Yaman Kumar Singla, Jayakumar Subramanian, Changyou Chen, Rajiv Ratn Shah, Zhiqiang Xu, Balaji Krishnamurthy

ICML 2025 Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Chengqian Gao, Haonan Li, Liu Liu, Zeke Xie, Peilin Zhao, Zhiqiang Xu

NeurIPS 2025 Unsupervised Trajectory Optimization for 3D Registration in Serial Section Electron Microscopy Using Neural ODEs Zhenbang Zhang, Jingtong Feng, Hongjia Li, Haythem El-Messiry, Zhiqiang Xu, Renmin Han

ICLR 2025 Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Bai LiChen, Shitong Shao, Zikai Zhou, Zipeng Qi, Zhiqiang Xu, Haoyi Xiong, Zeke Xie

ICLR 2024 AUC-CL: A Batchsize-Robust Framework for Self-Supervised Contrastive Representation Learning Rohan Sharma, Kaiyi Ji, Zhiqiang Xu, Changyou Chen

NeurIPS 2024 DALD: Improving Logits-Based Detector Without Logits from Black-Box LLMs Cong Zeng, Shengkun Tang, Xianjun Yang, Yuanzhou Chen, Yiyou Sun, Zhiqiang Xu, Yao Li, Haifeng Chen, Wei Cheng, Dongkuan Xu

IJCAI 2024 Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning Chengqian Gao, William de Vazelhes, Hualin Zhang, Bin Gu, Zhiqiang Xu

ICML 2024 Learning Constraints from Offline Demonstrations via Superior Distribution Correction Estimation Guorui Quan, Zhiqiang Xu, Guiliang Liu

ICLR 2024 Learning No-Regret Sparse Generalized Linear Models with Varying Observation(s) Diyang Li, Charles Ling, Zhiqiang Xu, Huan Xiong, Bin Gu

NeurIPS 2024 On the Comparison Between Multi-Modal and Single-Modal Contrastive Learning Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki

AAAI 2024 Prior and Prediction Inverse Kernel Transformer for Single Image Defocus Deblurring Peng Tang, Zhiqiang Xu, Chunlai Zhou, Pengfei Wei, Peng Han, Xin Cao, Tobias Lasser

ICML 2024 Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples Dake Bu, Wei Huang, Taiji Suzuki, Ji Cheng, Qingfu Zhang, Zhiqiang Xu, Hau-San Wong

NeurIPS 2023 Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels Jian Chen, Ruiyi Zhang, Tong Yu, Rohan Sharma, Zhiqiang Xu, Tong Sun, Changyou Chen

NeurIPS 2023 Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations Guanren Qiao, Guiliang Liu, Pascal Poupart, Zhiqiang Xu

AISTATS 2023 On the Accelerated Noise-Tolerant Power Method Zhiqiang Xu

NeurIPS 2023 On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, Masashi Sugiyama

NeurIPS 2023 Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective Pengfei Wei, Lingdong Kong, Xinghua Qu, Yi Ren, Zhiqiang Xu, Jing Jiang, Xiang Yin

ALT 2022 Faster Noisy Power Method Zhiqiang Xu, Ping Li

AAAI 2022 Local Differential Privacy for Belief Functions Qiyu Li, Chunlai Zhou, Biao Qin, Zhiqiang Xu

ACML 2022 Noisy Riemannian Gradient Descent for Eigenvalue Computation with Application to Inexact Stochastic Recursive Gradient Algorithm You-Lin Chen, Zhiqiang Xu, Ping Li

AISTATS 2021 On the Faster Alternating Least-Squares for CCA Zhiqiang Xu, Ping Li

NeurIPS 2021 A Comprehensively Tight Analysis of Gradient Descent for PCA Zhiqiang Xu, Ping Li

JMLR 2021 On the Riemannian Search for Eigenvector Computation Zhiqiang Xu, Ping Li

UAI 2020 A Practical Riemannian Algorithm for Computing Dominant Generalized Eigenspace Zhiqiang Xu, Ping Li

NeurIPS 2020 Towards Better Generalization of Adaptive Gradient Methods Yingxue Zhou, Belhal Karimi, Jinxing Yu, Zhiqiang Xu, Ping Li

NeurIPS 2019 Towards Practical Alternating Least-Squares for CCA Zhiqiang Xu, Ping Li

IJCAI 2018 Convergence Analysis of Gradient Descent for Eigenvector Computation Zhiqiang Xu, Xin Cao, Xin Gao

NeurIPS 2018 Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation Zhiqiang Xu

AISTATS 2018 On Truly Block Eigensolvers via Riemannian Optimization Zhiqiang Xu, Xin Gao

UAI 2017 A Fast Algorithm for Matrix Eigen-Decompositionn Zhiqiang Xu, Yiping Ke, Xin Gao

ICML 2016 Matrix Eigen-Decomposition via Doubly Stochastic Riemannian Optimization Zhiqiang Xu, Peilin Zhao, Jianneng Cao, Xiaoli Li

JMLR 2009 Marginal Likelihood Integrals for Mixtures of Independence Models Shaowei Lin, Bernd Sturmfels, Zhiqiang Xu