Zhiqiang Xu
35 papers · 2009–2025 · 13 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (10) π§ Keyword Pioneer π Conference Polyglot (13)
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(10)
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(9)
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(10)
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(3)
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(142)
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(8)
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Century Club
(35)
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Prolific Year
(5)
Conferences
NIPS (10)
ICLR (4)
ICML (4)
AAAI (3)
AISTATS (3)
EMNLP (2)
IJCAI (2)
JMLR (2)
ACML (1)
ALT (1)
ICCV (1)
INTERSPEECH (1)
UAI (1)
Top co-authors
Research topics
Keywords
riemannian optimization
(5)
convergence analysis
(3)
gradient descent
(3)
eigenvector computation
(3)
linear convergence
(3)
eigenvalue computation
(3)
canonical correlation analysis
(3)
stiefel manifold
(2)
power iteration
(2)
spectral gap
(2)
differential privacy
(2)
alternating least square
(2)
stochastic gradient
(2)
matrix computation
(2)
adversarial learning
(2)
principal component analysis
(2)
matrix decomposition
(2)
matrix factorization
(2)
diffusion model
(2)
matrix eigenvalue
(2)
Papers
A Gaussian Filter-Based 3D Registration Method for Series Section Electron Microscopy
AAAI 2025
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples
ICML 2025
Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection
ICLR 2025
Measuring And Improving Engagement of Text-to-Image Generation Models
ICLR 2025
Golden Noise for Diffusion Models: A Learning Framework
ICCV 2025
Learning Constraints from Offline Demonstrations via Superior Distribution Correction Estimation
ICML 2024
DALD: Improving Logits-based Detector without Logits from Black-box LLMs
NIPS 2024
On the Comparison between Multi-modal and Single-modal Contrastive Learning
NIPS 2024
Prior and Prediction Inverse Kernel Transformer for Single Image Defocus Deblurring
AAAI 2024
Visual Question Decomposition on Multimodal Large Language Models
EMNLP 2024
TextLap: Customizing Language Models for Text-to-Layout Planning
EMNLP 2024
AUC-CL: A Batchsize-Robust Framework for Self-Supervised Contrastive Representation Learning
ICLR 2024
Learning No-Regret Sparse Generalized Linear Models with Varying Observation(s)
ICLR 2024
Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning
IJCAI 2024
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples
ICML 2024
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective
NIPS 2023
Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
NIPS 2023
On the Accelerated Noise-Tolerant Power Method
AISTATS 2023
Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective
NIPS 2023
S2CD: Self-heuristic Speaker Content Disentanglement for Any-to-Any Voice Conversion
INTERSPEECH 2023
Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations
NIPS 2023
Noisy Riemannian Gradient Descent for Eigenvalue
Computation with Application to Inexact Stochastic
Recursive Gradient Algorithm
ACML 2022
Faster Noisy Power Method
ALT 2022
Local Differential Privacy for Belief Functions
AAAI 2022
On the Riemannian Search for Eigenvector Computation
JMLR 2021
A Comprehensively Tight Analysis of Gradient Descent for PCA
NIPS 2021
On the Faster Alternating Least-Squares for CCA
AISTATS 2021
A Practical Riemannian Algorithm for Computing Dominant Generalized Eigenspace
UAI 2020
Towards Better Generalization of Adaptive Gradient Methods
NIPS 2020
Towards Practical Alternating Least-Squares for CCA
NIPS 2019
Convergence Analysis of Gradient Descent for Eigenvector Computation
IJCAI 2018
On Truly Block Eigensolvers via Riemannian Optimization
AISTATS 2018
Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation
NIPS 2018
Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization
ICML 2016
Marginal Likelihood Integrals for Mixtures of Independence Models
JMLR 2009