b'Gilad Lerman'

research

∙ 09/02/2022

Cubic-Regularized Newton for Spectral Constrained Matrix Optimization and its Application to Fairness

Matrix functions are utilized to rewrite smooth spectral constrained mat...

0 Casey Garner, et al. ∙

research

∙ 06/17/2022

Robust Group Synchronization via Quadratic Programming

We propose a novel quadratic programming formulation for estimating the ...

0 Yunpeng Shi, et al. ∙

research

∙ 06/04/2022

An Unpooling Layer for Graph Generation

We propose a novel and trainable graph unpooling layer for effective gra...

0 Yinglong Guo, et al. ∙

research

∙ 03/30/2022

Fast, Accurate and Memory-Efficient Partial Permutation Synchronization

Previous partial permutation synchronization (PPS) algorithms, which are...

0 Shaohan Li, et al. ∙

research

∙ 03/17/2022

Stochastic and Private Nonconvex Outlier-Robust PCA

We develop theoretically guaranteed stochastic methods for outlier-robus...

0 Tyler Maunu, et al. ∙

research

∙ 02/04/2022

Robust Vector Quantized-Variational Autoencoder

Image generative models can learn the distributions of the training data...

6 Chieh-Hsin Lai, et al. ∙

research

∙ 01/13/2022

Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching

We develop new statistics for robustly filtering corrupted keypoint matc...

0 Yunpeng Shi, et al. ∙

research

∙ 09/07/2020

Ensemble Riemannian Data Assimilation over the Wasserstein Space

In this paper, we present a new ensemble data assimilation paradigm over...

0 Sagar K. Tamang, et al. ∙

research

∙ 07/27/2020

Message Passing Least Squares Framework and its Application to Rotation Synchronization

We propose an efficient algorithm for solving group synchronization unde...

0 Yunpeng Shi, et al. ∙

research

∙ 06/11/2020

Robust Multi-object Matching via Iterative Reweighting of the Graph Connection Laplacian

We propose an efficient and robust iterative solution to the multi-objec...

0 Yunpeng Shi, et al. ∙

research

∙ 06/09/2020

Novelty Detection via Robust Variational Autoencoding

We propose a new method for novelty detection that can tolerate nontrivi...

0 Chieh-Hsin Lai, et al. ∙

research

∙ 03/05/2020

Regularized Variational Data Assimilation for Bias Treatment using the Wasserstein Metric

This paper presents a new variational data assimilation (VDA) approach f...

0 Sagar K. Tamang, et al. ∙

research

∙ 02/13/2020

A Provably Robust Multiple Rotation Averaging Scheme for SO(2)

We give adversarial robustness results for synchronization on the rotati...

13 Tyler Maunu, et al. ∙

research

∙ 12/24/2019

Robust Group Synchronization via Cycle-Edge Message Passing

We propose a general framework for group synchronization with adversaria...

0 Gilad Lerman, et al. ∙

research

∙ 04/05/2019

Robust Subspace Recovery with Adversarial Outliers

We study the problem of robust subspace recovery (RSR) in the presence o...

0 Tyler Maunu, et al. ∙

research

∙ 03/30/2019

Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

We propose a neural network for unsupervised anomaly detection with a no...

0 Chieh-Hsin Lai, et al. ∙

research

∙ 01/15/2019

Algorithms for ℓ_p-based semi-supervised learning on graphs

We develop fast algorithms for solving the variational and game-theoreti...

12 Mauricio Flores Rios, et al. ∙

research

∙ 11/07/2018

Solving Jigsaw Puzzles By The Graph Connection Laplacian

We propose a novel mathematical framework to address the problem of auto...

2 Vahan Huroyan, et al. ∙

research

∙ 09/28/2018

Graph Generation via Scattering

Generative networks have made it possible to generate meaningful signals...

0 Dongmian Zou, et al. ∙

research

∙ 09/18/2018

Phase transition in random tensors with multiple spikes

Consider a spiked random tensor obtained as a mixture of two components:...

0 Wei-Kuo Chen, et al. ∙

research

∙ 04/07/2018

Estimation of Camera Locations in Highly Corrupted Scenarios: All About that Base, No Shape Trouble

We propose a strategy for improving camera location estimation in struct...

0 Yunpeng Shi, et al. ∙

research

∙ 03/31/2018

Graph Convolutional Neural Networks via Scattering

We generalize the scattering transform to graphs and consequently constr...

0 Dongmian Zou, et al. ∙

research

∙ 03/02/2018

An Overview of Robust Subspace Recovery

This paper will serve as an introduction to the body of work on robust s...

0 Gilad Lerman, et al. ∙

research

∙ 09/27/2017

Exact Camera Location Recovery by Least Unsquared Deviations

We establish exact recovery for the Least Unsquared Deviations (LUD) alg...

0 Gilad Lerman, et al. ∙

research

∙ 06/13/2017

A Well-Tempered Landscape for Non-convex Robust Subspace Recovery

We present a mathematical analysis of a non-convex energy landscape for ...

0 Tyler Maunu, et al. ∙

research

∙ 05/25/2017

Distributed Robust Subspace Recovery

We study Robust Subspace Recovery (RSR) in distributed settings. We cons...

0 Vahan Huroyan, et al. ∙

research

∙ 10/28/2015

Fast Landmark Subspace Clustering

Kernel methods obtain superb performance in terms of accuracy for variou...

0 Xu Wang, et al. ∙

research

∙ 10/01/2014

Riemannian Multi-Manifold Modeling

This paper advocates a novel framework for segmenting a dataset in a Rie...

0 Xu Wang, et al. ∙

research

∙ 06/24/2014

Fast, Robust and Non-convex Subspace Recovery

This work presents a fast and non-convex algorithm for robust subspace r...

0 Gilad Lerman, et al. ∙

research

∙ 05/09/2014

Better Feature Tracking Through Subspace Constraints

Feature tracking in video is a crucial task in computer vision. Usually,...

0 Bryan Poling, et al. ∙

research

∙ 04/10/2013

A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization

We present a new approach to rigid-body motion segmentation from two vie...

0 Bryan Poling, et al. ∙

research

∙ 01/09/2013

Spectral Clustering Based on Local PCA

We propose a spectral clustering method based on local principal compone...

0 Ery Arias-Castro, et al. ∙

research

∙ 02/18/2012

Robust computation of linear models by convex relaxation

Consider a dataset of vector-valued observations that consists of noisy ...

0 Gilad Lerman, et al. ∙

research

∙ 12/20/2011

A Novel M-Estimator for Robust PCA

We study the basic problem of robust subspace recovery. That is, we assu...

0 Teng Zhang, et al. ∙

research

∙ 04/19/2011

Robust recovery of multiple subspaces by geometric l_p minimization

We assume i.i.d. data sampled from a mixture distribution with K compone...

0 Gilad Lerman, et al. ∙

research

∙ 12/18/2010

lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers

We assume data sampled from a mixture of d-dimensional linear subspaces ...

0 Gilad Lerman, et al. ∙

research

∙ 10/17/2010

Hybrid Linear Modeling via Local Best-fit Flats

We present a simple and fast geometric method for modeling data by a uni...

0 Teng Zhang, et al. ∙

research

∙ 05/05/2010

Randomized hybrid linear modeling by local best-fit flats

The hybrid linear modeling problem is to identify a set of d-dimensional...

0 Teng Zhang, et al. ∙

research

∙ 02/09/2010

Probabilistic Recovery of Multiple Subspaces in Point Clouds by Geometric lp Minimization

We assume data independently sampled from a mixture distribution on the ...

0 Gilad Lerman, et al. ∙

research

∙ 01/08/2010

Spectral clustering based on local linear approximations

In the context of clustering, we assume a generative model where each cl...

0 Ery Arias-Castro, et al. ∙

research

∙ 09/16/2009

Median K-flats for hybrid linear modeling with many outliers

We describe the Median K-Flats (MKF) algorithm, a simple online method f...

0 Teng Zhang, et al. ∙

research

∙ 10/21/2008

Foundations of a Multi-way Spectral Clustering Framework for Hybrid Linear Modeling

The problem of Hybrid Linear Modeling (HLM) is to model and segment data...

0 Guangliang Chen, et al. ∙

Gilad Lerman

Featured Co-authors