986 citations · 2k across the 50 of their papers we have counts for
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Bayesian Gaussian Process ODEs via Double Normalizing Flows
Jian Xu, Shian Du, Junmei Yang +3
Recently, Gaussian processes have been used to model the vector field of continuous dynamical systems, referred to as GPODEs, which are characterized by a probabilistic ODE equatio…
Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks
Arunesh Mittal, Kai Yang, Paul Sajda +1
Several approximate inference methods have been proposed for deep discrete latent variable models. However, non-parametric methods which have previously been successfully employed…
Nonlinear Kalman Filtering with Reparametrization Gradients
San Gultekin, Brendan Kitts, Aaron Flores +1
We introduce a novel nonlinear Kalman filter that utilizes reparametrization gradients. The widely used parametric approximation is based on a jointly Gaussian assumption of the st…
Risk Bounds for Low Cost Bipartite Ranking
San Gultekin, John Paisley
Bipartite ranking is an important supervised learning problem; however, unlike regression or classification, it has a quadratic dependence on the number of samples. To circumvent t…
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou +1
Ensemble learning is a standard approach to building machine learning systems that capture complex phenomena in real-world data. An important aspect of these systems is the complet…
Random Function Priors for Correlation Modeling
Aonan Zhang, John Paisley
The likelihood model of high dimensional data can often be expressed as , where is a collection of hidden features shared…
Global Explanations of Neural Networks: Mapping the Landscape of Predictions
Mark Ibrahim, Melissa Louie, Ceena Modarres +1
A barrier to the wider adoption of neural networks is their lack of interpretability. While local explanation methods exist for one prediction, most global attributions still reduc…
Mixed Membership Recurrent Neural Networks
Ghazal Fazelnia, Mark Ibrahim, Ceena Modarres +2
Models for sequential data such as the recurrent neural network (RNN) often implicitly model a sequence as having a fixed time interval between observations and do not account for…
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process
Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou +1
Ensemble learning is a mainstay in modern data science practice. Conventional ensemble algorithms assigns to base models a set of deterministic, constant model weights that (1) do…
Towards Explainable Deep Learning for Credit Lending: A Case Study
Ceena Modarres, Mark Ibrahim, Melissa Louie +1
Deep learning adoption in the financial services industry has been limited due to a lack of model interpretability. However, several techniques have been proposed to explain predic…
MBA: Mini-Batch AUC Optimization
San Gultekin, Avishek Saha, Adwait Ratnaparkhi +1
Area under the receiver operating characteristics curve (AUC) is an important metric for a wide range of signal processing and machine learning problems, and scalable methods for o…
Online Forecasting Matrix Factorization
San Gultekin, John Paisley
In this paper the problem of forecasting high dimensional time series is considered. Such time series can be modeled as matrices where each column denotes a measurement. In additio…
Variational Bayesian Inference with Stochastic Search
John Paisley, David Blei, Michael Jordan
Mean-field variational inference is a method for approximate Bayesian posterior inference. It approximates a full posterior distribution with a factorized set of distributions by m…