activity
20152024
most citedDeep Closest Point: Learning Representations for Point Cloud Registration

120 citations · 433 across the 25 of their papers we have counts for

collaborators
Showing 2019Show all

12 papers · 1 filter

cs.LG201910 cited

Incorporating Unlabeled Data into Distributionally Robust Learning

Charlie Frogner, Sebastian Claici, Edward Chien +1

We study a robust alternative to empirical risk minimization called distributionally robust learning (DRL), in which one learns to perform against an adversary who can choose the d…

cs.LG20192 cited

Alleviating Label Switching with Optimal Transport

Pierre Monteiller, Sebastian Claici, Edward Chien +3

Label switching is a phenomenon arising in mixture model posterior inference that prevents one from meaningfully assessing posterior statistics using standard Monte Carlo procedure…

cs.CY2019

Recombination: A family of Markov chains for redistricting

Daryl DeFord, Moon Duchin, Justin Solomon

Redistricting is the problem of partitioning a set of geographical units into a fixed number of districts, subject to a list of often-vague rules and priorities. In recent years, t…

cs.LG2019112 cited

PRNet: Self-Supervised Learning for Partial-to-Partial Registration

Yue Wang, Justin M. Solomon

We present a simple, flexible, and general framework titled Partial Registration Network (PRNet), for partial-to-partial point cloud registration. Inspired by recently-proposed lea…

math.OC20194 cited

Geometry of Graph Partitions via Optimal Transport

Tara Abrishami, Nestor Guillen, Parker Rule +4

We define a distance metric between partitions of a graph using machinery from optimal transport. Our metric is built from a linear assignment problem that matches partition compon…

cs.GR2019

Algebraic Representations for Volumetric Frame Fields

David Palmer, David Bommes, Justin Solomon

Field-guided parametrization methods have proven effective for quad meshing of surfaces; these methods compute smooth cross fields to guide the meshing process and then integrate t…