102 citations · 251 across the 16 of their papers we have counts for
17 papers
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou +3
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approach…
Approximate blocked Gibbs sampling for Bayesian neural networks
Theodore Papamarkou
In this work, minibatch MCMC sampling for feedforward neural networks is made more feasible. To this end, it is proposed to sample subgroups of parameters via a blocked Gibbs sampl…
Topological Deep Learning: Going Beyond Graph Data
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou +11
Topological deep learning is a rapidly growing field that pertains to the development of deep learning models for data supported on topological domains such as simplicial complexes…
The Inverse Problem for Single Trajectories of Rough Differential Equations
Thomas Morrish, Theodore Papamarkou, Anastasia Papavasiliou +1
Motivated by the need to develop a general framework for performing statistical inference for discretely observed random rough differential equations, our aim is to construct a geo…
Probability-Generating Function Kernels for Spherical Data
Theodore Papamarkou, Alexey Lindo
Probability-generating function (PGF) kernels are introduced, which constitute a class of kernels supported on the unit hypersphere, for the purposes of spherical data analysis. PG…
A Random Persistence Diagram Generator
Theodore Papamarkou, Farzana Nasrin, Austin Lawson +3
Topological data analysis (TDA) studies the shape patterns of data. Persistent homology is a widely used method in TDA that summarizes homological features of data at multiple scal…
Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling
Adam Spannaus, Theodore Papamarkou, Samantha Erwin +1
The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiolo…
Bayesian neural networks and dimensionality reduction
Deborshee Sen, Theodore Papamarkou, David Dunson
In conducting non-linear dimensionality reduction and feature learning, it is common to suppose that the data lie near a lower-dimensional manifold. A class of model-based approach…
Hidden Markov models as recurrent neural networks: an application to Alzheimer's disease
Matt Baucum, Anahita Khojandi, Theodore Papamarkou
Hidden Markov models (HMMs) are commonly used for disease progression modeling when the true patient health state is not fully known. Since HMMs typically have multiple local optim…
Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks
Theodore Papamarkou, Hayley Guy, Bryce Kroencke +8
Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability…
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes
Devanshu Agrawal, Theodore Papamarkou, Jacob Hinkle
There has recently been much work on the "wide limit" of neural networks, where Bayesian neural networks (BNNs) are shown to converge to a Gaussian process (GP) as all hidden layer…
Challenges in Markov chain Monte Carlo for Bayesian neural networks
Theodore Papamarkou, Jacob Hinkle, M. Todd Young +1
Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the para…
Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem
Mathieu Besançon, Theodore Papamarkou, David Anthoff +4
Random variables and their distributions are a central part in many areas of statistical methods. The Distributions.jl package provides Julia users and developers tools for working…
The Efficiency of Geometric Samplers for Exoplanet Transit Timing Variation Models
Noah W. Tuchow, Eric B. Ford, Theodore Papamarkou +1
Transit timing variations (TTVs) are a valuable tool to determine the masses and orbits of transiting planets in multi-planet systems. TTVs can be readily modeled given knowledge o…
Geometric adaptive Monte Carlo in random environment
Theodore Papamarkou, Alexey Lindo, Eric B. Ford
Manifold Markov chain Monte Carlo algorithms have been introduced to sample more effectively from challenging target densities exhibiting multiple modes or strong correlations. Suc…
Forward-Mode Automatic Differentiation in Julia
Jarrett Revels, Miles Lubin, Theodore Papamarkou
We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring performance competitive with low-level languages like C++. Unlike recently develop…
The Controlled Thermodynamic Integral for Bayesian Model Comparison
Chris J. Oates, Theodore Papamarkou, Mark Girolami
Bayesian model comparison relies upon the model evidence, yet for many models of interest the model evidence is unavailable in closed form and must be approximated. Many of the est…