Yuling Jiao
+
Personal Information
- Name (Pinyin):Jiao Yuling
- Date of Birth:1986-07-01
- E-Mail:
- Date of Employment:2019-11-01
- Administrative Position:Professor
- Education Level:With Certificate of Graduation for Doctorate Study
- Business Address:Room 207, Northeast Building
- Gender:Male
- Contact Information:+86 15871394253
- Status:Employed
- Teacher College:School of Mathematics and Statistics
Other Contact Information
- email:
- [1]. A Primal Dual Active Set Algorithm with continuation in Compressed Sensing. IEEE Transactions on Signal Processing. 62 (33). 6276-6285. 2014. MATLAB code: http://xllv.whu.edu.cn/pdascl1.zip.
- [2]. A Primal Dual Active Set with Continuation Algorithm for the l0-Regularized Optimization Problem. Applied and Computational Harmonic Analysis. 39 (3). 400-426. 2015. MATALB code: http://www0.cs.ucl.ac.uk/staff/b.jin/software/pdascl0.zip.
- [3]. Alternating Direction Method of Multiplier for Linear Inverse Problems. SIAM Journal on Numerical Analysis. 54 (4). 2114-2137. 2016. MATLAB code: http://xllv.whu.edu.cn/admm_lin_inv.rar.
- [4]. Iterative Soft/Hard Thresholding Homotopy Algorithm for Sparse Recovery. IEEE Signal Processing Letter. 24 (6). 784-788. 2017. MATLAB code: http://www0.cs.ucl.ac.uk/staff/b.jin/software/ishtc.zip.
- [5]. Preasymptotic Convergence of Randomized Kaczmarz Method. Inverse Problems. 33 (12). 125012. 2017.
- [6]. Group Sparse Recovery via the l0(l2) Penalty: Theory and Algorithm. IEEE Transactions on Signal Processing. 65 (4). 998-1012. 2017. MATLAB code: http://www0.cs.ucl.ac.uk/staff/b.jin/software/gpdasc.zip.
- [7]. Preconditioned Alternating Direction Method of Multipliers for Solving Inverse Problems with Constraints. Inverse Problems. 33 (2). 025004. 2017.
- [8]. A Constructive Approach to L0 Penalized Regression. Journal of Machine Learning Research. 19 (10). 1-37. 2018. MATLAB code: see attachment.
- [9]. Robust Decoding from 1-bit Compressive Sampling by Ordinary and Regularized Least Squares. SIAM Journal on Scientific Computing. 40 (4). A2062-A2086. 2018. MATLAB code： see attachment..
- [10]. Deep Generative Learning via Variational Gradeint Flow. ICML. 97. 2093-2101. 2019. Pytorch code: https://github.com/xjtuygao/VGrow.
- [11]. Fitting Sparse Linear Models under the Sufficient and Necessary Condition for Model Identification. Statistics & Probability Letters. 168. 108925. 2020.
- [12]. A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies. Nucleic acids research. 48 (19). e109. 2020.
- [13]. Probabilistic embedding and clustering with alignment for spatial transcriptomics data integration with PRECAST. Nature Communications. 14 (1). 296. 2023.
- [14]. Generative Learning With Euler Particle Transport. MSML. 145. 1-33. 2021. Pytorch code: https://github.com/xjtuygao/EPT.
- [15]. A Unified Primal Dual Active Set Algorithm Nonconvex Sparse Recovery. Statistical Science. 36 (2). 215-238. 2021. MATLAB code: http://www0.cs.ucl.ac.uk/staff/b.jin/software/updasc.zip.
- [16]. REMI: Regression with Marginal Information and Its Application in Genome-wide Association Studies. Statistica Sinica. 31. 1985-2004. 2021. R code: https://github.com/gordonliu810822/REMI.
- [17]. Deep Generative Learning via Schrodinger Bridge. ICML. 10794-10804. 2021. Demo Pytorch code see attachment.
- [18]. Non-asymptotic Error Bounds for Bidirectional GANs. NeurIPS, 34, 12328-12339, 2021.
- [19]. An Error Analysis of Generative Adversarial Networks for Learning Distributions. Journal of Machine Learning Research. 23 (116). 1-43. 2022.
- [20]. A Deep Generative Approach to Conditional Sampling. Journal of the American Statistical Association. 118 (543). 1837-1848. 2023. Code: https://github.com/Jason-Xingyu/A-Deep-Generative-Approach-to-Conditional-Sampling.
- [21]. Approximation with Deep Convolutional Networks in Sobolev Space: with Applications to Classification. NeurIPS (Oral). 2022.
- [22]. Sample-Efficient Sparse Phase Retrieval via Stochastic Alternating Minimization. IEEE Transactions on Signal Processing. 70. 4951-4966. 2022.
- [23]. Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality on Holder Class. SIAM Journal on Mathematical Analysis. 55 (4). 3635-3649. 2023.
- [24]. Deep Nonparametric Regression on Approximately Low-dimensional Manifolds: Non-Asymptotic Error Bounds with Polynomail Prefactors. Annals of Statistics. 51 (2). 691-716. 2023.
- [25]. Just Least Squares: Binary Compressive Sampling with Low Generative Intrinsic Dimension. Journal of Scientific Computing. 95 (1). 28. 2023.
- [26]. Integrative Analysis for High-dimensional Stratified Models. Statistica Sinica. 33. 1533-1553. 2023.
- [27]. Convergence Rate Analysis for Deep Ritz Method. CICP. 31 (4). 1020-1048. 2022.
- [28]. A Rate of Convergence of Weak Adversarial Neural Networks for the Second Order Parabolic PDEs. Commun. Comput. Phys.. 34 (3). 814-836. 2023.
- [29]. Improved Analysis of PINNs: Alleviate the CoD for Compositional Solutions. Applied Annals of Mathematics. 39 (3). 239-263. 2023.
- [30]. Error Analysis of Deep Ritz Methods for Elliptic Equations. Analysis and Applications. 22 (1). 57-87. 2024.
- [31]. Deep Ritz Method for Elliptical Multiple Eigenvalue Problems. Journal of Scientific Computing. 98 (2). 48. 2024.
- [32]. Recovering the Source Term in Elliptic Equation via Deep Learning: Method and Convergence Analysis. East Asian Journal on Applied Mathematics. 19. 460-489. 2024.
- [33]. Error Estimation to the Direct Sampling Method for the Inverse Acoustic Source Problem with Multi-frequency Data. Inverse Problems and Imaging. 18 (2). 517-540. 2024.
- [34]. A Stabilized Physics Informed Neural Networks Method for Wave Equations. arXiv:2403.19090.
- [35]. A Gaussian Mixture Distribution-based Adaptive Sampling Method for Physics-informed Neural Networks. Engineering Applications of Artificial Intelligence. 135. 108770. 2024.
- [36]. Convergence Analysis for Over-Parameterized Deep Learning. Commun. Comput. Phys.. 36 (1). 71-103. 2024.
- [37]. Approximation Bounds for Norm Constrained Neural Networks with Applications to Regression and GANs. Applied and Computational Harmonic Analysis. 65. 249-278. 2023.
- [38]. Fast Excess Risk Rate via Offset Rademacher Complexity. ICML. 2023.
- [39]. Global Optimization via Schr {\" o} dinger-F {\" o} llmer Diffusion. SIAM Journal on Control and Optimization. 61 (5). 2953-2980. 2023.
- [40]. Deep Dimension Reduction for Supervised Representation Learning. IEEE Transactions on Information Theory. 70 (5). 3583-3598. 2024. Pytoch code: https://github.com/Liao-Xu/DDR.
- [41]. Neural Network Approximation for Pessimistic Offline Reinforcement Learning. AAAI. 2024.
- [42]. Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits. NeurIPS (Spotlight). 2024.
- [43]. Over parameterized Deep Nonparametric Regression for Dependent Data with Its Applications to Reinforcement Learning. Journal of Machine Learning Research. 24 (383). 1-40. 2023.
- [44]. Robust Decoding from Binary Measurements with Cardinality Constraint Least Squares. CICP (accepted), arXiv preprint arXiv:2006.02890.
- [45]. Nonparametric Estimation of Non-Crossing Quantile Regression Process with Deep Neural Networks. Journal of Machine Learning Research. 25 (88). 1-75. 2024.
- [46]. Relative Entropy Gradient Sampler for Unnormalized Distributions. Journal of Computational and Graphical Statistics (accepted).
- [47]. Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models. SIAM Journal on Mathematics of Data Science. 6 (4). 847-868. 2024.
- [48]. Deep Nonlinear Sufficient Dimention Reduction. Annals of Statistics. 52 (3). 1201-1226. 2024.
- [49]. Gaussian Interpolation Flows. Journal of Machine Learning Research. 25 (253). 1-52. 2024.
- [50]. Schrodinger-Follmer Sampler: Sampling without Ergodicity. arXiv preprint arXiv: 2106.10880. 2021. R code and Python code: https://github.com/Liao-Xu/SFS_R and https://github.com/Liao-Xu/SFS_py.
- [51]. Convergence Analysis of Schrodinger-Follmer Sampler without Convexity. arXiv preprint arXiv:2107.04766.
- [52]. Sampling via Föllmer Flow. arXiv preprint arXiv:2311.03660.
- [53]. Deep Approximate Policy Iteration. Submitted.
- [54]. Semi-supervised Deep Sobolev Regression: Estimation, Variable Selection and Beyond. arXiv:2401.04535.
- [55]. Deep Conditional Generative Learning: Model and Error Analysis. arXiv:2402.01460.
- [56]. Convergence of Continuous Normalizing Flows for Learning Probability Distributions. arXiv:2404.00551.
- [57]. Convergence Analysis of Flow Matching in Latent Space with Transformers. arXiv:2404.02538.
- [58]. Latent Schr{\"o}dinger Bridge Diffusion Model for Generative Learning. arXiv:2404.13309.
- [59]. Model Free Prediction with Uncertainty Assessment. arXiv:2405.12684.
- [60]. Characteristic Learning for Provable One Step Generation. arXiv:2405.05512.
- [61]. Unsupervised Transfer Learning via Adversarial Contrastive Training. arXiv: 2408.08533. 2024.
- [62]. DRM Revisited: A Complete Error Analysis. arXiv preprint arXiv:2407.09032. 2024.
| |