most citedGlobal Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression

5 citations · 7 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Search

Tianyu Huang, Haoang Li, Liangzu Peng +2

Estimating the rigid transformation with 6 degrees of freedom based on a putative 3D correspondence set is a crucial procedure in point cloud registration. Existing correspondence…

cs.CV2024

Scalable 3D Registration via Truncated Entry-wise Absolute Residuals

Tianyu Huang, Liangzu Peng, René Vidal +1

Given an input set of D point pairs, the goal of outlier-robust D registration is to compute some rotation and translation that align as many point pairs as possible. This is…

cs.LG2023

The Ideal Continual Learner: An Agent That Never Forgets

Liangzu Peng, Paris V. Giampouras, René Vidal

The goal of continual learning is to find a model that solves multiple learning tasks which are presented sequentially to the learner. A key challenge in this setting is that the l…

math.OC20225 cited

Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression

Liangzu Peng, Christian Kümmerle, René Vidal

We advance both the theory and practice of robust -quasinorm regression for by using novel variants of iteratively reweighted least-squares (IRLS) to solve th…

math.OC20222 cited

Towards Understanding The Semidefinite Relaxations of Truncated Least-Squares in Robust Rotation Search

Liangzu Peng, Mahyar Fazlyab, René Vidal

The rotation search problem aims to find a 3D rotation that best aligns a given number of point pairs. To induce robustness against outliers for rotation search, prior work conside…