469 citations · 2k across the 43 of their papers we have counts for
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Streaming Variational Inference for Bayesian Nonparametric Mixture Models
Alex Tank, Nicholas J. Foti, Emily B. Fox
In theory, Bayesian nonparametric (BNP) models are well suited to streaming data scenarios due to their ability to adapt model complexity with the observed data. Unfortunately, suc…
Stochastic Variational Inference for Hidden Markov Models
Nicholas J. Foti, Jason Xu, Dillon Laird +1
Variational inference algorithms have proven successful for Bayesian analysis in large data settings, with recent advances using stochastic variational inference (SVI). However, su…
Expectation-Maximization for Learning Determinantal Point Processes
Jennifer Gillenwater, Alex Kulesza, Emily Fox +1
A determinantal point process (DPP) is a probabilistic model of set diversity compactly parameterized by a positive semi-definite kernel matrix. To fit a DPP to a given task, we wo…
Learning the Parameters of Determinantal Point Process Kernels
Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams +1
Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired. While DPPs have many appealing p…
Modeling the Complex Dynamics and Changing Correlations of Epileptic Events
Drausin F. Wulsin, Emily B. Fox, Brian Litt
Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of eve…
Stochastic Gradient Hamiltonian Monte Carlo
Tianqi Chen, Emily B. Fox, Carlos Guestrin
Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling mo…