no code implementations • 31 Oct 2022 • Natalie Klein, Amber J. Day, Harris Mason, Michael W. Malone, Sinead A. Williamson
Our motivating example is magnetic resonance spectroscopy, in which a primary goal is to detect the presence of short-duration, low-amplitude radio frequency signals that are often obscured by strong interference that can be difficult to separate from the signal using traditional methods.
1 code implementation • 24 Jun 2020 • Sinead A. Williamson, Jette Henderson
Understanding how two datasets differ can help us determine whether one dataset under-represents certain sub-populations, and provides insights into how well models will generalize across datasets.
no code implementations • 15 Jun 2020 • Shorya Consul, Sinead A. Williamson
Privatizing these queries typically comes at a high utility cost, in large part because we are privatizing queries on small subsets of the data, which are easily corrupted by added noise.
no code implementations • 15 Jan 2020 • Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead A. Williamson
Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow.
no code implementations • 11 Oct 2019 • Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Graham W. Taylor, Sinead A. Williamson
As the availability and importance of temporal interaction data--such as email communication--increases, it becomes increasingly important to understand the underlying structure that underpins these interactions.
1 code implementation • 3 Sep 2019 • Guy W. Cole, Sinead A. Williamson
Classifiers that achieve demographic balance by explicitly using protected attributes such as race or gender are often politically or culturally controversial due to their lack of individual fairness, i. e. individuals with similar qualifications will receive different outcomes.
no code implementations • 28 May 2019 • Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Graham W. Taylor, Sinead A. Williamson
Interaction graphs, such as those recording emails between individuals or transactions between institutions, tend to be sparse yet structured, and often grow in an unbounded manner.
1 code implementation • 24 May 2019 • Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt
Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations arrive.
no code implementations • 18 Apr 2019 • Sinead A. Williamson, Michael Minyi Zhang, Paul Damien
These random, observed responses are typically affected by many unobserved, latent factors (or features) within the building such as the number of individuals, the turning on and off of electrical devices, power surges, etc.
no code implementations • 3 Apr 2019 • Guy W. Cole, Sinead A. Williamson
For example, in an email network, the volume of communication between two users and the content of that communication can give us information about both the strength and the nature of their relationship.
no code implementations • 11 Jan 2019 • Thom Lake, Sinead A. Williamson, Alexander T. Hawk, Christopher C. Johnson, Benjamin P. Wing
The application of machine learning techniques to large-scale personalized recommendation problems is a challenging task.
1 code implementation • 15 Oct 2018 • Sinead A. Williamson, Mauricio Tec
Large real-world graphs tend to be sparse, but they often contain densely connected subgraphs and exhibit high clustering coefficients.
Methodology
no code implementations • 7 Jun 2018 • Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson
Deep generative networks can simulate from a complex target distribution, by minimizing a loss with respect to samples from that distribution.
no code implementations • 19 May 2017 • Michael Minyi Zhang, Sinead A. Williamson, Fernando Perez-Cruz
First, we introduce an accelerated feature proposal mechanism that we show is a valid MCMC algorithm for posterior inference.
no code implementations • 9 Mar 2017 • Michael M. Zhang, Avinava Dubey, Sinead A. Williamson
In this paper we present a novel algorithm to perform asymptotically exact parallel Markov chain Monte Carlo inference for Indian Buffet Process models.
1 code implementation • 27 Feb 2017 • Michael Minyi Zhang, Sinead A. Williamson
Training Gaussian process-based models typically involves an $ O(N^3)$ computational bottleneck due to inverting the covariance matrix.
no code implementations • NeurIPS 2016 • Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabas Poczos, Alexander J. Smola, Eric P. Xing
In this paper, we present techniques for reducing variance in stochastic gradient Langevin dynamics, yielding novel stochastic Monte Carlo methods that improve performance by reducing the variance in the stochastic gradient.
no code implementations • NeurIPS 2014 • Kumar Avinava Dubey, Qirong Ho, Sinead A. Williamson, Eric P. Xing
Hierarchical clustering methods offer an intuitive and powerful way to model a wide variety of data sets.
no code implementations • NeurIPS 2013 • Sinead A. Williamson, Steve N. Maceachern, Eric P. Xing
Distributions over exchangeable matrices with infinitely many columns are useful in constructing nonparametric latent variable models.