1 citations · 1 across the 8 of their papers we have counts for
8 papers
SUN-DSBO: A Structured Unified Framework for Nonconvex Decentralized Stochastic Bilevel Optimization
Yaoshuai Ma, Xiao Wang, Wei Yao +1
Decentralized stochastic bilevel optimization (DSBO) is a powerful tool for various machine learning tasks, including decentralized meta-learning and hyperparameter tuning. Existin…
GEM: 3D Gaussian Splatting for Efficient and Accurate Cryo-EM Reconstruction
Huaizhi Qu, Xiao Wang, Gengwei Zhang +2
Cryo-electron microscopy (cryo-EM) has become a central tool for high-resolution structural biology, yet the massive scale of datasets (often exceeding 100k particle images) render…
DualHash: A Stochastic Primal-Dual Algorithm with Theoretical Guarantee for Deep Hashing
Luxuan Li, Xiao Wang, Chunfeng Cui
Deep hashing converts high-dimensional feature vectors into compact binary codes, enabling efficient large-scale retrieval. A fundamental challenge in deep hashing stems from the d…
PHASE: Physics-Integrated, Heterogeneity-Aware Surrogates for Scientific Simulations
Dawei Gao, Dali Wang, Zhuowei Gu +5
Large-scale numerical simulations underpin modern scientific discovery but remain constrained by prohibitive computational costs. AI surrogates offer acceleration, yet adoption in…
Faster stochastic cubic regularized Newton methods with momentum
Yiming Yang, Chuan He, Xiao Wang +1
Cubic regularized Newton (CRN) methods have attracted signiffcant research interest because they offer stronger solution guarantees and lower iteration complexity. With the rise of…
Block Coordinate Descent Methods for Optimization under J-Orthogonality Constraints with Applications
Di He, Ganzhao Yuan, Xiao Wang +1
The J-orthogonal matrix, also referred to as the hyperbolic orthogonal matrix, is a class of special orthogonal matrix in hyperbolic space, notable for its advantageous properties.…
Inductive Global and Local Manifold Approximation and Projection
Jungeum Kim, Xiao Wang
Nonlinear dimensional reduction with the manifold assumption, often called manifold learning, has proven its usefulness in a wide range of high-dimensional data analysis. The signi…
Implicit Generative Prior for Bayesian Neural Networks
Yijia Liu, Xiao Wang
Predictive uncertainty quantification is crucial for reliable decision-making in various applied domains. Bayesian neural networks offer a powerful framework for this task. However…