research
∙
07/26/2023
Learning sources of variability from high-dimensional observational studies
Causal inference studies whether the presence of a variable influences a...
share
research
∙
03/29/2023
Polarity is all you need to learn and transfer faster
Natural intelligences (NIs) thrive in a dynamic world - they learn quick...
research
∙
03/16/2023
Preserving Derivative Information while Transforming Neuronal Curves
The international neuroscience community is building the first comprehen...
research
∙
02/27/2023
Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis
We propose a class of models based on Fisher's Linear Discriminant (FLD)...
research
∙
08/23/2022
The Value of Out-of-Distribution Data
More data helps us generalize to a task. But real datasets can contain o...
research
∙
08/05/2022
Why do networks have inhibitory/negative connections?
Why do brains have inhibitory connections? Why do deep networks have neg...
research
∙
01/31/2022
Deep discriminative to kernel generative modeling
The fight between discriminative versus generative goes deep, in both th...
research
∙
01/19/2022
Prospective Learning: Back to the Future
Research on both natural intelligence (NI) and artificial intelligence (...
research
∙
11/09/2021
Graph Matching via Optimal Transport
The graph matching problem seeks to find an alignment between the nodes ...
research
∙
10/16/2021
Streaming Decision Trees and Forests
Machine learning has successfully leveraged modern data and provided com...
research
∙
09/29/2021
Towards a theory of out-of-distribution learning
What is learning? 20^st century formalizations of learning theory – whic...
research
∙
08/31/2021
When are Deep Networks really better than Random Forests at small sample sizes?
Random forests (RF) and deep networks (DN) are two of the most popular m...
research
∙
07/25/2021
Federated Causal Inference in Heterogeneous Observational Data
Analyzing observational data from multiple sources can be useful for inc...
research
∙
04/04/2021
Fitting Splines to Axonal Arbors Quantifies Relationship between Branch Order and Geometry
Neuromorphology is crucial to identifying neuronal subtypes and understa...
research
∙
11/12/2020
A partition-based similarity for classification distributions
Herein we define a measure of similarity between classification distribu...
research
∙
08/23/2020
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
This paper considers the graph signal processing problem of anomaly dete...
research
∙
07/27/2020
Robust Similarity and Distance Learning via Decision Forests
Canonical distances such as Euclidean distance often fail to capture the...
research
∙
05/25/2020
Statistical Analysis of Data Repeatability Measures
The advent of modern data collection and processing techniques has seen ...
research
∙
05/25/2020
mvlearn: Multiview Machine Learning in Python
As data are generated more and more from multiple disparate sources, mul...
research
∙
05/20/2020
Learning to rank via combining representations
Learning to rank – producing a ranked list of items specific to a query ...
research
∙
04/27/2020
A New Age of Computing and the Brain
The history of computer science and brain sciences are intertwined. In h...
research
∙
04/27/2020
A general approach to progressive intelligence
In biological learning, data is used to improve performance on the task ...
research
∙
04/27/2020
A general approach to progressive learning
In biological learning, data is used to improve performance on the task ...
research
∙
12/27/2019
The Chi-Square Test of Distance Correlation
Distance correlation has gained much recent attention in the statistics ...
research
∙
11/07/2019
Improving Power of 2-Sample Random Graph Tests with Applications in Connectomics
In many applications, there is an interest in testing whether two graphs...
research
∙
10/20/2019
The Exact Equivalence of Independence Testing and Two-Sample Testing
Testing independence and testing equality of distributions are two tight...
research
∙
09/25/2019
Manifold Forests: Closing the Gap on Neural Networks
Decision forests (DF), in particular random forests and gradient boostin...
research
∙
09/06/2019
AutoGMM: Automatic Gaussian Mixture Modeling in Python
Gaussian mixture modeling is a fundamental tool in clustering, as well a...
research
∙
07/07/2019
Graphyti: A Semi-External Memory Graph Library for FlashGraph
Graph datasets exceed the in-memory capacity of most standalone machines...
research
∙
07/05/2019
Geodesic Learning via Unsupervised Decision Forests
Geodesic distance is the shortest path between two points in a Riemannia...
research
∙
07/03/2019
hyppo: A Comprehensive Multivariate Hypothesis Testing Python Package
We introduce hyppo, a unified library for performing multivariate hypoth...
research
∙
07/03/2019
mgcpy: A Comprehensive High Dimensional Independence Testing Python Package
With the increase in the amount of data in many fields, a method to cons...
research
∙
06/24/2019
Inference for multiple heterogeneous networks with a common invariant subspace
The development of models for multiple heterogeneous network data is of ...
research
∙
06/09/2019
Graph Independence Testing
Identifying statistically significant dependency between variables is a ...
research
∙
02/24/2019
clusterNOR: A NUMA-Optimized Clustering Framework
Clustering algorithms are iterative and have complex data access pattern...
research
∙
11/30/2018
Decision Forests Induce Characteristic Kernels
Decision forests are popular tools for classification and regression. Th...
research
∙
08/23/2018
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Clustering is concerned with coherently grouping observations without an...
research
∙
06/19/2018
Forest Packing: Fast, Parallel Decision Forests
Machine learning has an emerging critical role in high-performance compu...
research
∙
06/14/2018
The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing
Distance-based methods, also called "energy statistics", are leading met...
research
∙
01/23/2018
Signal Subgraph Estimation Via Vertex Screening
Graph classification and regression have wide applications in a variety ...
research
∙
10/26/2017
Energy Clustering
Energy statistics was proposed by Székely in the 80's inspired by the Ne...
research
∙
10/26/2017
From Distance Correlation to Multiscale Generalized Correlation
Understanding and developing a correlation measure that can detect gener...
research
∙
09/16/2017
Statistical inference on random dot product graphs: a survey
The random dot product graph (RDPG) is an independent-edge random graph ...
research
∙
09/05/2017
Linear Optimal Low Rank Projection for High-Dimensional Multi-Class Data
Classification of individual samples into one or more categories is crit...
research
∙
05/09/2017
Semiparametric spectral modeling of the Drosophila connectome
We present semiparametric spectral modeling of the complete larval Droso...
research
∙
03/10/2017
Joint Embedding of Graphs
Feature extraction and dimension reduction for networks is critical in a...
research
∙
12/01/2016
A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information
CLARITY is a method for converting biological tissues into translucent a...
research
∙
11/16/2016
Probabilistic Fluorescence-Based Synapse Detection
Brain function results from communication between neurons connected by c...
research
∙
09/16/2016
Discovering Relationships and their Structures Across Disparate Data Modalities
Determining whether certain properties are related to other properties i...
research
∙
09/06/2016
