Search Results for author: Hayden S. Helm

Found 9 papers, 3 papers with code

Mental State Classification Using Multi-graph Features

no code implementations25 Feb 2022 Guodong Chen, Hayden S. Helm, Kate Lytvynets, Weiwei Yang, Carey E. Priebe

We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive load.

Classification EEG +1

Towards a theory of out-of-distribution learning

no code implementations29 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).

Learning Theory

Inducing a hierarchy for multi-class classification problems

no code implementations20 Feb 2021 Hayden S. Helm, Weiwei Yang, Sujeeth Bharadwaj, Kate Lytvynets, Oriana Riva, Christopher White, Ali Geisa, Carey E. Priebe

In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not.

Classification General Classification +1

Subgraph nomination: Query by Example Subgraph Retrieval in Networks

no code implementations29 Jan 2021 Al-Fahad M. Al-Qadhi, Carey E. Priebe, Hayden S. Helm, Vince Lyzinski

This paper introduces the subgraph nomination inference task, in which example subgraphs of interest are used to query a network for similarly interesting subgraphs.

Recommendation Systems

A partition-based similarity for classification distributions

no code implementations12 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.

Classification General Classification +2

Learning to rank via combining representations

2 code implementations20 May 2020 Hayden S. Helm, Amitabh Basu, Avanti Athreya, Youngser Park, Joshua T. Vogelstein, Michael Winding, Marta Zlatic, Albert Cardona, Patrick Bourke, Jonathan Larson, Chris White, Carey E. Priebe

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.

Learning-To-Rank

Omnidirectional Transfer for Quasilinear Lifelong Learning

1 code implementation27 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.

Federated Learning Transfer Learning

GraSPy: Graph Statistics in Python

1 code implementation29 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.