Combining Geometric and Topological Information in Image Segmentation. - Hengrui Luo; Justin D. Strait; Abhijoy Saha (CoRR, 2019)

Generalized Penalty for Circular Coordinate Representation. - Hengrui Luo; Alice Patania; Jisu Kim; Mikael Vejdemo-Johansson (CoRR, 2020)

Topological Learning for Motion Data via Mixed Coordinates. - Hengrui Luo; Jisu Kim; Alice Patania; Mikael Vejdemo-Johansson (IEEE BigData, 2021)

Combining Geometric and Topological Information for Boundary Estimation. - Hengrui Luo; Justin D. Strait (IEEE BigData, 2021)

Enhancing Autotuning Capability with a History Database. - Younghyun Cho; James Demmel; Xiaoye S. Li; Yang Liu 179; Hengrui Luo (MCSoC, 2021)

Non-smooth Bayesian Optimization in Tuning Problems. - Hengrui Luo; James Weldon Demmel; Younghyun Cho; Xiaoye S. Li; Yang Liu 179 (CoRR, 2021)

Spherical Rotation Dimension Reduction with Geometric Loss Functions. - Hengrui Luo; Didong Li (CoRR, 2022)

Hybrid Models for Mixed Variables in Bayesian Optimization. - Hengrui Luo; Younghyun Cho; James Weldon Demmel; Xiaoye S. Li; Yang Liu 179 (CoRR, 2022)

Nonparametric Multi-shape Modeling with Uncertainty Quantification. - Hengrui Luo; Justin D. Strait (CoRR, 2022)

Sparse Additive Gaussian Process Regression. - Hengrui Luo; Giovanni Nattino; Matthew T. Pratola (J. Mach. Learn. Res., 2022)

A Distance-Preserving Matrix Sketch. - Leland Wilkinson; Hengrui Luo (J. Comput. Graph. Stat., 2022)

Harnessing the Crowd for Autotuning High-Performance Computing Applications. - Younghyun Cho; James Weldon Demmel; Jacob King; Xiaoye S. Li; Yang Liu 179; Hengrui Luo (IPDPS, 2023)

Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software. - Riley Murray; James Demmel; Michael W. Mahoney; N. Benjamin Erichson; Maksim Melnichenko; Osman Asif Malik; Laura Grigori; Piotr Luszczek; Michal Derezinski; Miles E. Lopes; Tianyu Liang; Hengrui Luo; et al. (CoRR, 2023)

Contrastive inverse regression for dimension reduction. - Sam Hawke; Hengrui Luo; Didong Li (CoRR, 2023)

Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization. - Yin-Ting Liao; Hengrui Luo; Anna Ma (CoRR, 2023)

Sharded Bayesian Additive Regression Trees. - Hengrui Luo; Matthew T. Pratola (CoRR, 2023)

A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes. - Marcus Michael Noack; Hengrui Luo; Mark D. Risser (CoRR, 2023)

Tensor Decision Trees For High-Resolution Imaging Data. - Zhihao Chen; Hengrui Luo; Suprateek Kundu; Marina Vannucci (IEEE Big Data, 2024)

Efficient Decision Trees for Tensor Regressions. - Hengrui Luo; Akira Horiguchi; Li Ma (CoRR, 2024)

Frontal Slice Approaches for Tensor Linear Systems. - Hengrui Luo; Anna Ma (CoRR, 2024)

Ranking Perspective for Tree-based Methods with Applications to Symbolic Feature Selection. - Hengrui Luo; Meng Li (CoRR, 2024)

A Linear-complexity Tensor Butterfly Algorithm for Compressing High-dimensional Oscillatory Integral Operators. - P. Michael Kielstra; Tianyi Shi; Hengrui Luo; Jianliang Qian; Yang Liu 179 (CoRR, 2024)

Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data. - Mark D. Risser; Marcus Michael Noack; Hengrui Luo; Ronald Pandolfi (CoRR, 2024)

Non-smooth Bayesian optimization in tuning scientific applications. - Hengrui Luo; Younghyun Cho; James Weldon Demmel; Igor Kozachenko; Xiaoye S. Li; Yang Liu 179 (Int. J. High Perform. Comput. Appl., 2024)

Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization. - Hengrui Luo; Younghyun Cho; James Weldon Demmel; Xiaoye S. Li; Yang Liu 179 (J. Comput. Graph. Stat., 2024)

Spherical Rotation Dimension Reduction with Geometric Loss Functions. - Hengrui Luo; Jeremy E. Purvis; Didong Li (J. Mach. Learn. Res., 2024)

Multiple Closed Curve Modeling with Uncertainty Quantification for Shape Analysis. - Hengrui Luo; Justin D. Strait (SIAM/ASA J. Uncertain. Quantification, 2024)

Surrogate-Based Autotuning for Randomized Sketching Algorithms in Regression Problems. - Younghyun Cho; James Demmel; Michal Derezinski; Haoyun Li; Hengrui Luo; Michael W. Mahoney; Riley Murray (SIAM J. Matrix Anal. Appl., 2025)

Asymptotic Optimism of Random-Design Linear and Kernel Regression Models. - Hengrui Luo; Yunzhang Zhu (CoRR, 2025)

Kernel-based estimators for functional causal effects. - Yordan P. Raykov; Hengrui Luo; Justin D. Strait; Wasiur R. KhudaBukhsh (CoRR, 2025)

EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data. - Jason Han; Nicholas S. DiBrita; Younghyun Cho; Hengrui Luo; Tirthak Patel (CoRR, 2025)

Parallel GPU-Accelerated Randomized Construction of Approximate Cholesky Preconditioners. - Tianyu Liang; Chao Chen; Yotam Yaniv; Hengrui Luo; David Tench; Xiaoye S. Li; Aydin Buluç; James Demmel (CoRR, 2025)

Minimizing the Arithmetic and Communication Complexity of Jacobi's Method for Eigenvalues and Singular Values. - James Demmel; Hengrui Luo; Ryan Schneider; Yifu Wang (CoRR, 2025)