380 citations · 1.1k across the 43 of their papers we have counts for
25 papers · 1 filter
Posterior Collapse and Latent Variable Non-identifiability
Yixin Wang, David M. Blei, John P. Cunningham
Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network.…
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome
Elliott Gordon-Rodriguez, Thomas P. Quinn, John P. Cunningham
Data augmentation plays a key role in modern machine learning pipelines. While numerous augmentation strategies have been studied in the context of computer vision and natural lang…
On the Normalizing Constant of the Continuous Categorical Distribution
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Andres Potapczynski +1
Probability distributions supported on the simplex enjoy a wide range of applications across statistics and machine learning. Recently, a novel family of such distributions has bee…
Rectangular Flows for Manifold Learning
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss +1
Normalizing flows are invertible neural networks with tractable change-of-volume terms, which allow optimization of their parameters to be efficiently performed via maximum likelih…
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Geoff Pleiss +1
Modern deep learning is primarily an experimental science, in which empirical advances occasionally come at the expense of probabilistic rigor. Here we focus on one such example; n…
Linear-time inference for Gaussian Processes on one dimension
Jackson Loper, David Blei, John P. Cunningham +1
Gaussian Processes (GPs) provide powerful probabilistic frameworks for interpolation, forecasting, and smoothing, but have been hampered by computational scaling issues. Here we in…
The continuous categorical: a novel simplex-valued exponential family
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, John P. Cunningham
Simplex-valued data appear throughout statistics and machine learning, for example in the context of transfer learning and compression of deep networks. Existing models for this cl…
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
The Gumbel-Softmax is a continuous distribution over the simplex that is often used as a relaxation of discrete distributions. Because it can be readily interpreted and easily repa…
The continuous Bernoulli: fixing a pervasive error in variational autoencoders
Gabriel Loaiza-Ganem, John P. Cunningham
Variational autoencoders (VAE) have quickly become a central tool in machine learning, applicable to a broad range of data types and latent variable models. By far the most common…
Approximating exponential family models (not single distributions) with a two-network architecture
Sean R. Bittner, John P. Cunningham
Recently much attention has been paid to deep generative models, since they have been used to great success for variational inference, generation of complex data types, and more. I…
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data
Gabriel Loaiza-Ganem, Sean M. Perkins, Karen E. Schroeder +2
Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to…
A Probabilistic Model of Cardiac Physiology and Electrocardiograms
Andrew C. Miller, Ziad Obermeyer, David M. Blei +2
An electrocardiogram (EKG) is a common, non-invasive test that measures the electrical activity of a patient's heart. EKGs contain useful diagnostic information about patient healt…
Nonlinear Evolution via Spatially-Dependent Linear Dynamics for Electrophysiology and Calcium Data
Daniel Hernandez, Antonio Khalil Moretti, Ziqiang Wei +3
Latent variable models have been widely applied for the analysis of time series resulting from experimental neuroscience techniques. In these datasets, observations are relatively…
Calibrating Deep Convolutional Gaussian Processes
Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham +2
The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the abili…
Bayesian estimation for large scale multivariate Ornstein-Uhlenbeck model of brain connectivity
Andrea Insabato, John P. Cunningham, Matthieu Gilson
Estimation of reliable whole-brain connectivity is a crucial step towards the use of connectivity information in quantitative approaches to the study of neuropsychiatric disorders.…
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Scott W. Linderman, Gonzalo E. Mena, Hal Cooper +2
Many matching, tracking, sorting, and ranking problems require probabilistic reasoning about possible permutations, a set that grows factorially with dimension. Combinatorial optim…
Bayesian Learning of Kernel Embeddings
Seth Flaxman, Dino Sejdinovic, John P. Cunningham +1
Kernel methods are one of the mainstays of machine learning, but the problem of kernel learning remains challenging, with only a few heuristics and very little theory. This is of p…
Preconditioning Kernel Matrices
Kurt Cutajar, Michael A. Osborne, John P. Cunningham +1
The computational and storage complexity of kernel machines presents the primary barrier to their scaling to large, modern, datasets. A common way to tackle the scalability issue i…
Black box variational inference for state space models
Evan Archer, Il Memming Park, Lars Buesing +2
Latent variable time-series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure…
Neuroprosthetic decoder training as imitation learning
Josh Merel, David Carlson, Liam Paninski +1
Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In…
Sparse Probit Linear Mixed Model
Stephan Mandt, Florian Wenzel, Shinichi Nakajima +3
Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a conti…
Linear Dimensionality Reduction: Survey, Insights, and Generalizations
John P. Cunningham, Zoubin Ghahramani
Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational p…
GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes
Andrew Gordon Wilson, Elad Gilboa, Arye Nehorai +1
Gaussian processes are typically used for smoothing and interpolation on small datasets. We introduce a new Bayesian nonparametric framework -- GPatt -- enabling automatic pattern…
Scaling Multidimensional Inference for Structured Gaussian Processes
Elad Gilboa, Yunus Saatçi, John P. Cunningham
Exact Gaussian Process (GP) regression has O(N^3) runtime for data size N, making it intractable for large N. Many algorithms for improving GP scaling approximate the covariance wi…