3 papers
stat.ML2025
Transformed Regularizations for Robust Principal Component Analysis: Toward a Fine-Grained Understanding
Kun Zhao, Haoke Zhang, Jiayi Wang +1
Robust Principal Component Analysis (RPCA) aims to recover a low-rank structure from noisy, partially observed data that is also corrupted by sparse, potentially large-magnitude ou…
cs.LG2025
A comparative analysis of rank aggregation methods for the partial label ranking problem
Jiayi Wang, Juan C. Alfaro, Viktor Bengs
The label ranking problem is a supervised learning scenario in which the learner predicts a total order of the class labels for a given input instance. Recently, research has incre…
math.ST2025
Noisy Low-Rank Matrix Completion via Transformed Regularization and its Theoretical Properties
Kun Zhao, Jiayi Wang, Yifei Lou
This paper focuses on recovering an underlying matrix from its noisy partial entries, a problem commonly known as matrix completion. We delve into the investigation of a non-convex…