1 code implementation • 26 Jul 2023 • Eric W. Bridgeford, Jaewon Chung, Brian Gilbert, Sambit Panda, Adam Li, Cencheng Shen, Alexandra Badea, Brian Caffo, Joshua T. Vogelstein

Causal inference studies whether the presence of a variable influences an observed outcome.

1 code implementation • 29 Mar 2023 • Qingyang Wang, Michael A. Powell, Ali Geisa, Eric W. Bridgeford, Joshua T. Vogelstein

Natural intelligences (NIs) thrive in a dynamic world - they learn quickly, sometimes with only a few samples.

no code implementations • 16 Mar 2023 • Thomas L. Athey, Daniel J. Tward, Ulrich Mueller, Laurent Younes, Joshua T. Vogelstein, Michael I. Miller

Then, the traces are mapped to common coordinate systems by transforming the positions of their points, which neglects how the transformation bends the line segments in between.

no code implementations • 27 Feb 2023 • Hayden S. Helm, Ashwin De Silva, Joshua T. Vogelstein, Carey E. Priebe, Weiwei Yang

We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation.

1 code implementation • 23 Aug 2022 • Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein

In this work, we show a counter-intuitive phenomenon: the generalization error of a task can be a non-monotonic function of the number of OOD samples.

no code implementations • ICCV 2023 • Qingyang Wang, Michael A. Powell, Ali Geisa, Eric Bridgeford, Carey E. Priebe, Joshua T. Vogelstein

Why do deep networks have negative weights?

1 code implementation • 31 Jan 2022 • Jayanta Dey, Will LeVine, Haoyin Xu, Ashwin De Silva, Tyler M. Tomita, Ali Geisa, Tiffany Chu, Jacob Desman, Joshua T. Vogelstein

However, these methods are not calibrated for the entire feature space, leading to overconfidence in the case of out-of-distribution (OOD) samples.

no code implementations • 19 Jan 2022 • Ashwin De Silva, Rahul Ramesh, Lyle Ungar, Marshall Hussain Shuler, Noah J. Cowan, Michael Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan, Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu, Timothy Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein

We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.

1 code implementation • 9 Nov 2021 • Ali Saad-Eldin, Benjamin D. Pedigo, Carey E. Priebe, Joshua T. Vogelstein

The graph matching problem seeks to find an alignment between the nodes of two graphs that minimizes the number of adjacency disagreements.

2 code implementations • 16 Oct 2021 • Haoyin Xu, Jayanta Dey, Sambit Panda, Joshua T. Vogelstein

In a benchmark suite containing 72 classification problems (the OpenML-CC18 data suite), we illustrate that our approach, Stream Decision Forest (SDF), does not suffer from either of the aforementioned limitations.

no code implementations • 29 Sep 2021 • Ali Geisa, Ronak Mehta, Hayden S. Helm, Jayanta Dey, Eric Eaton, Jeffery Dick, Carey E. Priebe, Joshua T. Vogelstein

This assumption renders these theories inadequate for characterizing 21$^{st}$ century real world data problems, which are typically characterized by evaluation distributions that differ from the training data distributions (referred to as out-of-distribution learning).

2 code implementations • 31 Aug 2021 • Haoyin Xu, Kaleab A. Kinfu, Will LeVine, Sambit Panda, Jayanta Dey, Michael Ainsworth, Yu-Chung Peng, Madi Kusmanov, Florian Engert, Christopher M. White, Joshua T. Vogelstein, Carey E. Priebe

Empirically, we compare these two strategies on hundreds of tabular data settings, as well as several vision and auditory settings.

1 code implementation • 25 Jul 2021 • Ruoxuan Xiong, Allison Koenecke, Michael Powell, Zhu Shen, Joshua T. Vogelstein, Susan Athey

We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site.

no code implementations • 4 Jun 2021 • Thomas L. Athey, Daniel J. Tward, Ulrich Mueller, Joshua T. Vogelstein, Michael I. Miller

Our most probable estimation method models the task of reconstructing neuronal processes in the presence of other neurons, and thus is applicable in images with several neurons.

no code implementations • 4 Apr 2021 • Thomas L. Athey, Jacopo Teneggi, Joshua T. Vogelstein, Daniel Tward, Ulrich Mueller, Michael I. Miller

Our representation makes it possible to compute these parameters from neuron traces in closed form.

1 code implementation • 30 Nov 2020 • Vivek Gopalakrishnan, Jaewon Chung, Eric Bridgeford, Benjamin D. Pedigo, Jesús Arroyo, Lucy Upchurch, G. Allan Johnson, Nian Wang, Youngser Park, Carey E. Priebe, Joshua T. Vogelstein

A connectome is a map of the structural and/or functional connections in the brain.

no code implementations • 12 Nov 2020 • Hayden S. Helm, Ronak D. Mehta, Brandon Duderstadt, Weiwei Yang, Christoper M. White, Ali Geisa, Joshua T. Vogelstein, Carey E. Priebe

Herein we define a measure of similarity between classification distributions that is both principled from the perspective of statistical pattern recognition and useful from the perspective of machine learning practitioners.

1 code implementation • 23 Aug 2020 • Guodong Chen, Jesús Arroyo, Avanti Athreya, Joshua Cape, Joshua T. Vogelstein, Youngser Park, Chris White, Jonathan Larson, Weiwei Yang, Carey E. Priebe

We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices.

Methodology

no code implementations • 27 Jul 2020 • Tyler M. Tomita, Joshua T. Vogelstein

Many algorithms have been proposed for automated learning of suitable distances, most of which employ linear methods to learn a global metric over the feature space.

no code implementations • 25 May 2020 • Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein

As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have ballooned in recent years.

2 code implementations • 20 May 2020 • Hayden S. Helm, Amitabh Basu, Avanti Athreya, Youngser Park, Joshua T. Vogelstein, Carey E. Priebe, Michael Winding, Marta Zlatic, Albert Cardona, Patrick Bourke, Jonathan Larson, Marah Abdin, Piali Choudhury, Weiwei Yang, Christopher W. White

Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest.

1 code implementation • 27 Apr 2020 • Joshua T. Vogelstein, Jayanta Dey, Hayden S. Helm, Will LeVine, Ronak D. Mehta, Ali Geisa, Haoyin Xu, Gido M. van de Ven, Emily Chang, Chenyu Gao, Weiwei Yang, Bryan Tower, Jonathan Larson, Christopher M. White, Carey E. Priebe

But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data.

no code implementations • 27 Apr 2020 • Polina Golland, Jack Gallant, Greg Hager, Hanspeter Pfister, Christos Papadimitriou, Stefan Schaal, Joshua T. Vogelstein

In December 2014, a two-day workshop supported by the Computing Community Consortium (CCC) and the National Science Foundation's Computer and Information Science and Engineering Directorate (NSF CISE) was convened in Washington, DC, with the goal of bringing together computer scientists and brain researchers to explore these new opportunities and connections, and develop a new, modern dialogue between the two research communities.

no code implementations • 21 Apr 2020 • Allison Koenecke, Michael Powell, Ruoxuan Xiong, Zhu Shen, Nicole Fischer, Sakibul Huq, Adham M. Khalafallah, Marco Trevisan, Pär Sparen, Juan J Carrero, Akihiko Nishimura, Brian Caffo, Elizabeth A. Stuart, Renyuan Bai, Verena Staedtke, David L. Thomas, Nickolas Papadopoulos, Kenneth W. Kinzler, Bert Vogelstein, Shibin Zhou, Chetan Bettegowda, Maximilian F. Konig, Brett Mensh, Joshua T. Vogelstein, Susan Athey

Here, we conducted retrospective analyses in two cohorts of patients with acute respiratory distress (ARD, n=18, 547) and three cohorts with pneumonia (n=400, 907).

1 code implementation • 27 Dec 2019 • Cencheng Shen, Sambit Panda, Joshua T. Vogelstein

One major bottleneck is the testing process: because the null distribution of distance correlation depends on the underlying random variables and metric choice, it typically requires a permutation test to estimate the null and compute the p-value, which is very costly for large amount of data.

no code implementations • 20 Oct 2019 • Sambit Panda, Cencheng Shen, Ronan Perry, Jelle Zorn, Antoine Lutz, Carey E. Priebe, Joshua T. Vogelstein

The evaluation included several popular independence statistics and covered a comprehensive set of simulations.

1 code implementation • 25 Sep 2019 • Adam Li, Ronan Perry, Chester Huynh, Tyler M. Tomita, Ronak Mehta, Jesus Arroyo, Jesse Patsolic, Benjamin Falk, Joshua T. Vogelstein

In particular, Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to a permutation of the feature indices.

1 code implementation • 6 Sep 2019 • Thomas L. Athey, Tingshan Liu, Benjamin D. Pedigo, Joshua T. Vogelstein

Background: Gaussian mixture modeling is a fundamental tool in clustering, as well as discriminant analysis and semiparametric density estimation.

no code implementations • 18 Aug 2019 • Cencheng Shen, Jaewon Chung, Ronak Mehta, Ting Xu, Joshua T. Vogelstein

While many non-parametric and universally consistent dependence measures have recently been proposed, directly applying them to temporal data can inflate the p-value and result in invalid test.

no code implementations • 7 Jul 2019 • Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns

Emerging frameworks avoid the network bottleneck of distributed data with Semi-External Memory (SEM) that uses a single multicore node and operates on graphs larger than memory.

Distributed, Parallel, and Cluster Computing Databases

no code implementations • 5 Jul 2019 • Meghana Madhyastha, Percy Li, James Browne, Veronika Strnadova-Neeley, Carey E. Priebe, Randal Burns, Joshua T. Vogelstein

Empirical results on simulated and real data demonstrate that URerF is robust to high-dimensional noise, where as other methods, such as Isomap, UMAP, and FLANN, quickly deteriorate in such settings.

4 code implementations • 3 Jul 2019 • Sambit Panda, Satish Palaniappan, Junhao Xiong, Eric W. Bridgeford, Ronak Mehta, Cencheng Shen, Joshua T. Vogelstein

We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing.

1 code implementation • 30 Jun 2019 • Ronan Perry, Ronak Mehta, Richard Guo, Eva Yezerets, Jesús Arroyo, Mike Powell, Hayden Helm, Cencheng Shen, Joshua T. Vogelstein

Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty.

2 code implementations • 29 Mar 2019 • Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Hayden S. Helm, Joshua T. Vogelstein

We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations.

no code implementations • 30 Nov 2018 • Sambit Panda, Cencheng Shen, Joshua T. Vogelstein

Decision forests are widely used for classification and regression tasks.

no code implementations • 23 Aug 2018 • Carey E. Priebe, Youngser Park, Joshua T. Vogelstein, John M. Conroy, Vince Lyzinski, Minh Tang, Avanti Athreya, Joshua Cape, Eric Bridgeford

Clustering is concerned with coherently grouping observations without any explicit concept of true groupings.

no code implementations • 14 Jun 2018 • Cencheng Shen, Joshua T. Vogelstein

Distance-based tests, also called "energy statistics", are leading methods for two-sample and independence tests from the statistics community.

no code implementations • 9 Apr 2018 • Randal Burns, Eric Perlman, Alex Baden, William Gray Roncal, Ben Falk, Vikram Chandrashekhar, Forrest Collman, Sharmishtaa Seshamani, Jesse Patsolic, Kunal Lillaney, Michael Kazhdan, Robert Hider Jr., Derek Pryor, Jordan Matelsky, Timothy Gion, Priya Manavalan, Brock Wester, Mark Chevillet, Eric T. Trautman, Khaled Khairy, Eric Bridgeford, Dean M. Kleissas, Daniel J. Tward, Ailey K. Crow, Matthew A. Wright, Michael I. Miller, Stephen J. Smith, R. Jacob Vogelstein, Karl Deisseroth, Joshua T. Vogelstein

Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies.

Neurons and Cognition Quantitative Methods

no code implementations • 9 Mar 2018 • Gregory Kiar, Robert J. Anderson, Alex Baden, Alexandra Badea, Eric W. Bridgeford, Andrew Champion, Vikram Chandrashekhar, Forrest Collman, Brandon Duderstadt, Alan C. Evans, Florian Engert, Benjamin Falk, Tristan Glatard, William R. Gray Roncal, David N. Kennedy, Jeremy Maitin-Shepard, Ryan A. Marren, Onyeka Nnaemeka, Eric Perlman, Sharmishtaas Seshamani, Eric T. Trautman, Daniel J. Tward, Pedro Antonio Valdés-Sosa, Qing Wang, Michael I. Miller, Randal Burns, Joshua T. Vogelstein

Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales.

1 code implementation • 26 Oct 2017 • Cencheng Shen, Carey E. Priebe, Joshua T. Vogelstein

Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age.

no code implementations • 26 Oct 2017 • Guilherme França, Maria L. Rizzo, Joshua T. Vogelstein

In this paper, we consider a formulation for the clustering problem using a weighted version of energy statistics in spaces of negative type.

no code implementations • 16 Sep 2017 • Avanti Athreya, Donniell E. Fishkind, Keith Levin, Vince Lyzinski, Youngser Park, Yichen Qin, Daniel L. Sussman, Minh Tang, Joshua T. Vogelstein, Carey E. Priebe

In this survey paper, we describe a comprehensive paradigm for statistical inference on random dot product graphs, a paradigm centered on spectral embeddings of adjacency and Laplacian matrices.

1 code implementation • 5 Sep 2017 • Joshua T. Vogelstein, Eric Bridgeford, Minh Tang, Da Zheng, Christopher Douville, Randal Burns, Mauro Maggioni

To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences.

1 code implementation • 9 May 2017 • Carey E. Priebe, Youngser Park, Minh Tang, Avanti Athreya, Vince Lyzinski, Joshua T. Vogelstein, Yichen Qin, Ben Cocanougher, Katharina Eichler, Marta Zlatic, Albert Cardona

We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome.

2 code implementations • 10 Mar 2017 • Shangsi Wang, Jesús Arroyo, Joshua T. Vogelstein, Carey E. Priebe

Feature extraction and dimension reduction for networks is critical in a wide variety of domains.

no code implementations • 1 Dec 2016 • Kwame S. Kutten, Nicolas Charon, Michael I. Miller, J. T. Ratnanather, Jordan Matelsky, Alexander D. Baden, Kunal Lillaney, Karl Deisseroth, Li Ye, Joshua T. Vogelstein

Due to the novelty of this microscopy technique it is impractical to use absolute intensity values to align these images to existing standard atlases.

no code implementations • 16 Nov 2016 • Anish K. Simhal, Cecilia Aguerrebere, Forrest Collman, Joshua T. Vogelstein, Kristina D. Micheva, Richard J. Weinberg, Stephen J. Smith, Guillermo Sapiro

The present work describes new probabilistic image analysis methods for single-synapse analysis of synapse populations in both animal and human brains.

4 code implementations • 16 Sep 2016 • Joshua T. Vogelstein, Eric Bridgeford, Qing Wang, Carey E. Priebe, Mauro Maggioni, Cencheng Shen

Understanding the relationships between different properties of data, such as whether a connectome or genome has information about disease status, is becoming increasingly important in modern biological datasets.

no code implementations • 6 Sep 2016 • Runze Tang, Michael Ketcha, Alexandra Badea, Evan D. Calabrese, Daniel S. Margulies, Joshua T. Vogelstein, Carey E. Priebe, Daniel L. Sussman

In statistical connectomics, the quantitative study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem.

1 code implementation • 28 Jun 2016 • Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns

The \textit{k-means NUMA Optimized Routine} (\textsf{knor}) library has (i) in-memory (\textsf{knori}), (ii) distributed memory (\textsf{knord}), and (iii) semi-external memory (\textsf{knors}) modules that radically improve the performance of k-means for varying memory and hardware budgets.

Distributed, Parallel, and Cluster Computing

1 code implementation • 6 May 2016 • Kwame S. Kutten, Joshua T. Vogelstein, Nicolas Charon, Li Ye, Karl Deisseroth, Michael I. Miller

Therefore, we developed a method (Mask-LDDMM) for registering CLARITY images, that automatically find the brain boundary and learns the optimal deformation between the brain and atlas masks.

2 code implementations • 21 Apr 2016 • Da Zheng, Disa Mhembere, Joshua T. Vogelstein, Carey E. Priebe, Randal Burns

R is one of the most popular programming languages for statistics and machine learning, but the R framework is relatively slow and unable to scale to large datasets.

Distributed, Parallel, and Cluster Computing

no code implementations • 13 Apr 2016 • Eva L. Dyer, William Gray Roncal, Hugo L. Fernandes, Doga Gürsoy, Vincent De Andrade, Rafael Vescovi, Kamel Fezzaa, Xianghui Xiao, Joshua T. Vogelstein, Chris Jacobsen, Konrad P. Körding, Narayanan Kasthuri

Methods for resolving the 3D microstructure of the brain typically start by thinly slicing and staining the brain, and then imaging each individual section with visible light photons or electrons.

2 code implementations • 10 Jun 2015 • Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse L. Patsolic, Benjamin Falk, Jason Yim, Carey E. Priebe, Randal Burns, Mauro Maggioni, Joshua T. Vogelstein

Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency.

1 code implementation • 12 Dec 2014 • Cencheng Shen, Joshua T. Vogelstein, Carey E. Priebe

Then the shortest-path distance within each modality is calculated from the joint neighborhood graph, followed by embedding into and matching in a common low-dimensional Euclidean space.

no code implementations • 25 Nov 2014 • William Gray Roncal, Dean M. Kleissas, Joshua T. Vogelstein, Priya Manavalan, Kunal Lillaney, Michael Pekala, Randal Burns, R. Jacob Vogelstein, Carey E. Priebe, Mark A. Chevillet, Gregory D. Hager

Finally, we deploy a reference end-to-end version of the pipeline on a large, publicly available data set.

no code implementations • 8 Nov 2014 • Norbert Binkiewicz, Joshua T. Vogelstein, Karl Rohe

We utilize these node covariates to help uncover latent communities in a graph, using a modification of spectral clustering.

no code implementations • 13 May 2014 • Vince Lyzinski, Donniell Fishkind, Marcelo Fiori, Joshua T. Vogelstein, Carey E. Priebe, Guillermo Sapiro

Indeed, experimental results illuminate and corroborate these theoretical findings, demonstrating that excellent results are achieved in both benchmark and real data problems by amalgamating the two approaches.

no code implementations • 16 Apr 2014 • Ayushi Sinha, William Gray Roncal, Narayanan Kasthuri, Ming Chuang, Priya Manavalan, Dean M. Kleissas, Joshua T. Vogelstein, R. Jacob Vogelstein, Randal Burns, Jeff W. Lichtman, Michael Kazhdan

The contribution of this work is the introduction of a straightforward and robust pipeline which annotates axoplasmic reticula with high precision, contributing towards advancements in automatic feature annotations in neural EM data.

no code implementations • 14 Mar 2014 • William Gray Roncal, Michael Pekala, Verena Kaynig-Fittkau, Dean M. Kleissas, Joshua T. Vogelstein, Hanspeter Pfister, Randal Burns, R. Jacob Vogelstein, Mark A. Chevillet, Gregory D. Hager

An open challenge problem at the forefront of modern neuroscience is to obtain a comprehensive mapping of the neural pathways that underlie human brain function; an enhanced understanding of the wiring diagram of the brain promises to lead to new breakthroughs in diagnosing and treating neurological disorders.

no code implementations • 16 Jan 2014 • Heather Patsolic, Sancar Adali, Joshua T. Vogelstein, Youngser Park, Carey E. Friebe, Gongkai Li, Vince Lyzinski

We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm.

no code implementations • NeurIPS 2013 • David E. Carlson, Vinayak Rao, Joshua T. Vogelstein, Lawrence Carin

With simultaneous measurements from ever increasing populations of neurons, there is a growing need for sophisticated tools to recover signals from individual neurons.

1 code implementation • 4 Oct 2013 • Vince Lyzinski, Daniel L. Sussman, Donniell E. Fishkind, Henry Pao, Li Chen, Joshua T. Vogelstein, Youngser Park, Carey E. Priebe

We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs.

no code implementations • 22 Apr 2013 • Bruno Cornelis, Yun Yang, Joshua T. Vogelstein, Ann Dooms, Ingrid Daubechies, David Dunson

The preservation of our cultural heritage is of paramount importance.

no code implementations • 15 Nov 2012 • Carey E. Priebe, Daniel L. Sussman, Minh Tang, Joshua T. Vogelstein

Thus we errorfully observe $G$ when we observe the graph $\widetilde{G} = (V,\widetilde{E})$ as the edges in $\widetilde{E}$ arise from the classifications of the "edge-features", and are expected to be errorful.

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