research
∙
03/03/2023
Spectral learning of Bernoulli linear dynamical systems models for decision-making
Latent linear dynamical systems with Bernoulli observations provide a po...
share
research
∙
02/27/2022
Bayesian Active Learning for Discrete Latent Variable Models
Active learning seeks to reduce the number of samples required to estima...
research
∙
01/10/2022
Loss-calibrated expectation propagation for approximate Bayesian decision-making
Approximate Bayesian inference methods provide a powerful suite of tools...
research
∙
01/13/2020
Unifying and generalizing models of neural dynamics during decision-making
An open question in systems and computational neuroscience is how neural...
research
∙
06/07/2019
Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations
Gaussian Process Factor Analysis (GPFA) has been broadly applied to the ...
research
∙
11/28/2018
Shared Representational Geometry Across Neural Networks
Different neural networks trained on the same dataset often learn simila...
research
∙
11/28/2017
Dependent relevance determination for smooth and structured sparse regression
In many problem settings, parameter vectors are not merely sparse, but d...
research
∙
03/31/2017
Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature
An exciting branch of machine learning research focuses on methods for l...
research
∙
10/26/2016
