Yijun Dong (董一珺)
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Courant Instructor/Assistant Professor (postdoc) |
About Me
I am a Courant Instructor/Assistant Professor (postdoc) at the Courant Institute of New York University since 2023. I completed my PhD at the Oden Institute of UT Austin, advised by Prof. Per-Gunnar Martinsson and Prof. Rachel Ward.
My research lies in randomized numerical linear algebra and theoretical machine learning. Specifically, I am interested in the computational and sample efficiency of algorithms in machine learning and scientific computing. From the computational efficiency perspective, my work is centered on matrix sketching and randomized low-rank decompositions like SVD and CUR. From the sample efficiency perspective, my work focuses on the generalization and distributional robustness of learning algorithms in data-limited settings.
(Curriculum Vitae, Google Scholar, GitHub)
News
2023/09: Our paper got accepted at NeurIPS 2023: Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering with Kevin Miller, Qi Lei, Rachel Ward.
2023/04: Our paper got accepted at ICML 2023: Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift with Yuege Xie, Rachel Ward.
2023/03: I defended my thesis on ‘‘Randomized Dimension Reduction with Statistical Guarantees’’ at UT Austin. [dissertation, slides]
Recent Works
(* Equal contribution)
Robust Blockwise Random Pivoting: Fast and Accurate Adaptive Interpolative Decomposition
Yijun Dong, Chao Chen, Per-Gunnar Martinsson, Katherine Pearce. 2023. [GitHub]Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering.
Yijun Dong*, Kevin Miller*, Qi Lei, Rachel Ward.
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS) 2023. [GitHub]
Efficient Bounds and Estimates for Canonical Angles in Randomized Subspace Approximations.
Yijun Dong, Per-Gunnar Martinsson, Yuji Nakatsukasa. 2022. [GitHub]
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift.
Yijun Dong*, Yuege Xie*, Rachel Ward.
International Conference on Machine Learning (ICML) 2023. [GitHub, poster]
Sample Efficiency of Data Augmentation Consistency Regularization.
Shuo Yang*, Yijun Dong*, Rachel Ward, Inderjit Dhillon, Sujay Sanghavi, Qi Lei.
International Conference on Artificial Intelligence and Statistics (AISTATS) 2023. [pmlr]
Simpler is better: A comparative study of randomized algorithms for computing the CUR decomposition.
Yijun Dong, Per-Gunnar Martinsson.
Advances in Computational Mathematics 2023. [GitHub]
Education
Ph.D. in Computational Science, Engineering, and Mathematics, 2018 - 2023
Oden Institute for Computational Engineering and Sciences, UT Austin, Austin, Texas, US
Thesis: Randomized Dimension Reduction with Statistical Guarantees
B.S. in Applied Mathematics & Engineering Science, 2014 - 2018
Emory University, Atlanta, Georgia, US
Thesis: Crystals and Liquids in Gravitationally Confined Quasi-2-Dimensional Colloidal Systems
