6 citations · 25 across the 13 of their papers we have counts for
13 papers
A Statistical Turing Test for Generative Models
Hayden Helm, Carey E. Priebe, Weiwei Yang
The emergence of human-like abilities of AI systems for content generation in domains such as text, audio, and vision has prompted the development of classifiers to determine wheth…
Approximately optimal domain adaptation with Fisher's Linear Discriminant
Hayden S. Helm, Ashwin De Silva, Joshua T. Vogelstein +2
We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation. The class is the convex combination of two hypotheses: i) an average h…
Deep Learning with Label Noise: A Hierarchical Approach
Li Chen, Ningyuan Huang, Cong Mu +4
Deep neural networks are susceptible to label noise. Existing methods to improve robustness, such as meta-learning and regularization, usually require significant change to the net…
Mental State Classification Using Multi-graph Features
Guodong Chen, Hayden S. Helm, Kate Lytvynets +2
We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states suc…
Prospective Learning: Principled Extrapolation to the Future
Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…
Leveraging semantically similar queries for ranking via combining representations
Hayden S. Helm, Marah Abdin, Benjamin D. Pedigo +8
In modern ranking problems, different and disparate representations of the items to be ranked are often available. It is sensible, then, to try to combine these representations to…
Learning without gradient descent encoded by the dynamics of a neurobiological model
Vivek Kurien George, Vikash Morar, Weiwei Yang +6
The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss functi…
Inducing a hierarchy for multi-class classification problems
Hayden S. Helm, Weiwei Yang, Sujeeth Bharadwaj +5
In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not. Un-fortunately, the…
A partition-based similarity for classification distributions
Hayden S. Helm, Ronak D. Mehta, Brandon Duderstadt +5
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 pe…
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
Guodong Chen, Jesús Arroyo, Avanti Athreya +7
This paper considers the graph signal processing problem of anomaly detection in time series of graphs. We examine two related, complementary inference tasks: the detection of anom…
Distance-based Positive and Unlabeled Learning for Ranking
Hayden S. Helm, Amitabh Basu, Avanti Athreya +12
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. The setting we consider…
Design of a Privacy-Preserving Data Platform for Collaboration Against Human Trafficking
Darren Edge, Weiwei Yang, Kate Lytvynets +4
Case records on victims of human trafficking are highly sensitive, yet the ability to share such data is critical to evidence-based practice and policy development across governmen…
Simple Lifelong Learning Machines
Jayanta Dey, Joshua T. Vogelstein, Hayden S. Helm +13
In lifelong learning, data are used to improve performance not only on the present task, but also on past and future (unencountered) tasks. While typical transfer learning algorith…