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08/21/2023
Approximately Equivariant Graph Networks
Graph neural networks (GNNs) are commonly described as being permutation...
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06/24/2023
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning
Self-supervised learning converts raw perceptual data such as images to ...
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06/06/2023
Fine-grained Expressivity of Graph Neural Networks
Numerous recent works have analyzed the expressive power of message-pass...
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05/21/2023
GeometricImageNet: Extending convolutional neural networks to vector and tensor images
Convolutional neural networks and their ilk have been very successful fo...
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01/31/2023
The passive symmetries of machine learning
Any representation of data involves arbitrary investigator choices. Beca...
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11/28/2022
Sketch-and-solve approaches to k-means clustering by semidefinite programming
We introduce a sketch-and-solve approach to speed up the Peng-Wei semide...
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11/06/2022
Graph Neural Networks for Community Detection on Sparse Graphs
Spectral methods provide consistent estimators for community detection i...
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10/26/2022
Deep Learning is Provably Robust to Symmetric Label Noise
Deep neural networks (DNNs) are capable of perfectly fitting the trainin...
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09/30/2022
Shuffled linear regression through graduated convex relaxation
The shuffled linear regression problem aims to recover linear relationsh...
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09/29/2022
Equivariant maps from invariant functions
In equivariant machine learning the idea is to restrict the learning to ...
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09/24/2022
From Local to Global: Spectral-Inspired Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-...
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07/28/2022
MarkerMap: nonlinear marker selection for single-cell studies
Single-cell RNA-seq data allow the quantification of cell type differenc...
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04/02/2022
Dimensionless machine learning: Imposing exact units equivariance
Units equivariance is the exact symmetry that follows from the requireme...
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01/18/2022
A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants
Graph neural networks are designed to learn functions on graphs. Typical...
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10/07/2021
A simple equivariant machine learning method for dynamics based on scalars
Physical systems obey strict symmetry principles. We expect that machine...
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06/11/2021
Scalars are universal: Gauge-equivariant machine learning, structured like classical physics
There has been enormous progress in the last few years in designing conc...
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01/15/2021
Fitting very flexible models: Linear regression with large numbers of parameters
There are many uses for linear fitting; the context here is interpolatio...
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11/23/2020
Dimensionality reduction, regularization, and generalization in overparameterized regressions
Overparameterization in deep learning is powerful: Very large models fit...
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02/10/2020
Can graph neural networks count substructures?
The ability to detect and count certain substructures in graphs is impor...
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01/06/2020
MREC: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data
Comparing and aligning large datasets is a pervasive problem occurring a...
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01/06/2020
MREC: a fast and versatile framework for aligning and matching data with applications to single cell molecular data
Comparing and aligning large datasets is a pervasive problem occurring a...
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08/15/2019
Experimental performance of graph neural networks on random instances of max-cut
This note explores the applicability of unsupervised machine learning te...
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05/29/2019
On the equivalence between graph isomorphism testing and function approximation with GNNs
Graph neural networks (GNNs) have achieved lots of success on graph-stru...
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12/14/2018
Utility Ghost: Gamified redistricting with partisan symmetry
Inspired by the word game Ghost, we propose a new protocol for bipartisa...
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12/06/2018
SqueezeFit: Label-aware dimensionality reduction by semidefinite programming
Given labeled points in a high-dimensional vector space, we seek a low-d...
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08/27/2018
Fair redistricting is hard
Gerrymandering is a long-standing issue within the U.S. political system...
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03/25/2018
SUNLayer: Stable denoising with generative networks
It has been experimentally established that deep neural networks can be ...
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10/03/2017
Monte Carlo approximation certificates for k-means clustering
Efficient algorithms for k-means clustering frequently converge to subop...
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07/16/2017
Projected Power Iteration for Network Alignment
The network alignment problem asks for the best correspondence between t...
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06/22/2017
A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks
Many inverse problems are formulated as optimization problems over certa...
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10/17/2016
A polynomial-time relaxation of the Gromov-Hausdorff distance
The Gromov-Hausdorff distance provides a metric on the set of isometry c...
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02/22/2016
Clustering subgaussian mixtures by semidefinite programming
We introduce a model-free relax-and-round algorithm for k-means clusteri...
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05/18/2015
