Research output per year
Research output per year
Personal profile
Author IDs
ORCID iD: 0009-0007-3862-7756
Scopus Author ID: 57219689045
Impact
Qualifications (Brief)
BSc (Shandong University), MSc (University of Washington), PhD (University of Toronto)
Biography
Lu YU holds a PhD in Statistics from the University of Toronto and an MS degree in Statistics at the University of Washington. Prior to joining CityUHK, she was a Postdoctoral Fellow at the Center for Research in Economics and Statistics (CREST)/ENSAE Paris.
Lu’s research expertise spans machine learning, optimization, and statistics, with a strong emphasis on addressing real-world challenges in data science. Her work aims to develop theoretical foundations that closely align with practical applications in machine learning and optimization. She has published in prominent journals such as the Journal of Econometrics and Bernoulli, and top machine learning conferences including NeurIPS, COLT, and ICLR.
Position(s) Available
I am currently looking for PhD students with a strong background in analysis and probability, as well as a deep interest in theoretical research. For more information, please visit my personal website.
Research Interests/Areas
- Machine learning theory
- Stochastic optimization
- High-dimensional statistics
Related Links
Fingerprint
Dive into the research topics where Lu YU is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
- 1 Similar Profiles
Collaborations from the last five years
Recent external collaboration based on locations. Dive into details by clicking on the dots or
Research output
-
Advancing wasserstein convergence analysis of score-based models: Insights from discretization and second-order acceleration
Yu, Y. & Yu, L., 2025, Advances in Neural Information Processing Systems 38: NeurIPS 2025. D. Belgrave, C. Zhang, H. Lin, R. Pascanu, P. Koniusz, M. Ghassemi & N. Chen (eds.). Vol. 38.Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
-
Parallelized midpoint randomization for Langevin Monte Carlo
Yu, L. & Dalalyan, A., Dec 2025, In: Stochastic Processes and their Applications. 190, 27 p., 104764.Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Open AccessFile2 Link opens in a new tab Citations (Scopus)2 Downloads (CityUHK Scholars) -
LANGEVIN MONTE CARLO FOR STRONGLY LOG-CONCAVE DISTRIBUTIONS: RANDOMIZED MID-POINT REVISITED
Yu, L., Karagulyan, A. & Dalalyan, A., May 2024, The Twelfth International Conference on Learning Representations, ICLR 2024. International Conference on Learning Representations, ICLR, 37 p. (International Conference on Learning Representations, ICLR).Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
-
Spectral clustering with variance information for group structure estimation in panel data
Yu, L., Gu, J. & Volgushev, S., Apr 2024, In: Journal of Econometrics. 241, 1, 105709.Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
2 Link opens in a new tab Citations (Scopus) -
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance
Vural, N. M., Yu, L., Balasubramanian, K., Volgushev, S. & Erdogdu, M. A., Jul 2022, Conference on Learning Theory, 2-5 July 2022, London, UK. p. 65-102 (Proceedings of Machine Learning Research; vol. 178).Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Open Access18 Link opens in a new tab Citations (Scopus)
Projects
- 1 Active
-
ECS: Towards Mathematical Foundations of Diffusion Models
YU, L. (Principal Investigator / Project Coordinator)
1/09/25 → …
Project: Research