Rui Tuo - RankMe

34 papers A* 2A 2Journal 30

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	BI-DCGAN: A Theoretically Grounded Bayesian Framework for Efficient and Diverse GANs. CoRR Mahsa Valizadeh, Rui Tuo, James Caverlee
2025	A	`conf`	From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation. AISTATS Wenyuan Zhao, Haoyuan Chen, Tie Liu, Rui Tuo, Chao Tian
2025	J	`jnl`	From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation. CoRR Wenyuan Zhao, Haoyuan Chen, Tie Liu, Rui Tuo, Chao Tian
2025	J	`jnl`	Language Models as Semantic Augmenters for Sequential Recommenders. CoRR Mahsa Valizadeh, Xiangjue Dong, Rui Tuo, James Caverlee
2024	J	`jnl`	A General Theory for Kernel Packets: from state space model to compactly supported basis. CoRR Liang Ding, Rui Tuo
2024	J	`jnl`	Aggregation Models with Optimal Weights for Distributed Gaussian Processes. CoRR Haoyuan Chen, Rui Tuo
2024	J	`jnl`	Renewing Iterative Self-Labeling Domain Adaptation With Application to the Spine Motion Prediction. IEEE Trans Autom. Sci. Eng. Gecheng Chen, Yu Zhou, Xudong Zhang, Rui Tuo
2023	J	`jnl`	Privacy-aware Gaussian Process Regression. CoRR Rui Tuo, Raktim Bhattacharya
2023	J	`jnl`	The Temporal Overfitting Problem with Applications in Wind Power Curve Modeling. Technometrics Abhinav Prakash, Rui Tuo, Yu Ding
2022	J	`jnl`	Gaussian Process-Aided Function Comparison Using Noisy Scattered Data. Technometrics Abhinav Prakash, Rui Tuo, Yu Ding
2022	J	`jnl`	Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations. CoRR Haoyuan Chen, Liang Ding, Rui Tuo
2022	J	`jnl`	Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations. J. Mach. Learn. Res. Haoyuan Chen, Liang Ding, Rui Tuo
2022	J	`jnl`	Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction. CoRR Gecheng Chen, Yu Zhou, Xudong Zhang, Rui Tuo
2022	A	`conf`	Uncertainty Quantification for Bayesian Optimization. AISTATS Rui Tuo, Wenjia Wang
2021	J	`jnl`	A Sparse Expansion For Deep Gaussian Processes. CoRR Liang Ding, Rui Tuo, Shahin Shahrampour
2021	J	`jnl`	High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids. CoRR Liang Ding, Rui Tuo, Xiaowei Zhang
2021	J	`jnl`	Single and Multiple Change-Point Detection with Differential Privacy. J. Mach. Learn. Res. Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings
2020	A*	`conf`	Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features. ICML Liang Ding, Rui Tuo, Shahin Shahrampour
2020	J	`jnl`	Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features. CoRR Liang Ding, Rui Tuo, Shahin Shahrampour
2020	J	`jnl`	Kriging Prediction with Isotropic Matern Correlations: Robustness and Experimental Designs. J. Mach. Learn. Res. Rui Tuo, Wenjia Wang
2020	J	`jnl`	On the Improved Rates of Convergence for Matérn-Type Kernel Ridge Regression with Application to Calibration of Computer Models. SIAM/ASA J. Uncertain. Quantification Rui Tuo, Yan Wang, C. F. Jeff Wu
2020	J	`jnl`	Overcoming the Curse of Dimensionality in Density Estimation with Mixed Sobolev GANs. CoRR Liang Ding, Rui Tuo, Shahin Shahrampour
2020	J	`jnl`	Projection Pursuit Gaussian Process Regression. CoRR Gecheng Chen, Rui Tuo
2019	J	`jnl`	Adjustments to Computer Models via Projected Kernel Calibration. SIAM/ASA J. Uncertain. Quantification Rui Tuo
2019	J	`jnl`	Deterministic Sampling of Expensive Posteriors Using Minimum Energy Designs. Technometrics V. Roshan Joseph, Dianpeng Wang, Li Gu, Shiji Lyu, Rui Tuo
2018	A*	`conf`	Differentially Private Change-Point Detection. NeurIPS Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang
2018	J	`jnl`	Differentially Private Change-Point Detection. CoRR Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang
2018	J	`jnl`	Stochastic Convergence of a Nonconforming Finite Element Method for the Thin Plate Spline Smoother for Observational Data. SIAM J. Numer. Anal. Zhiming Chen, Rui Tuo, Wenlong Zhang
2017	J	`jnl`	Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion. Technometrics Xu He, Rui Tuo, C. F. Jeff Wu
2016	J	`jnl`	A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties. SIAM/ASA J. Uncertain. Quantification Rui Tuo, C. F. Jeff Wu
2015	J	`jnl`	Sequential Exploration of Complex Surfaces Using Minimum Energy Designs. Technometrics V. Roshan Joseph, Tirthankar Dasgupta, Rui Tuo, C. F. Jeff Wu
2014	J	`jnl`	Building Accurate Emulators for Stochastic Simulations via Quantile Kriging. Technometrics Matthew Plumlee, Rui Tuo
2014	J	`jnl`	Surrogate Modeling of Computer Experiments With Different Mesh Densities. Technometrics Rui Tuo, C. F. Jeff Wu, Dan Yu
2013	J	`jnl`	Comment: A Brownian Motion Model for Stochastic Simulation With Tunable Precision. Technometrics Rui Tuo, Peter Z. G. Qian, C. F. Jeff Wu