Search Results for author: Babak Shahbaba

Found 10 papers, 3 papers with code

Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks

no code implementations11 Jan 2021 Shiwei Lan, Shuyi Li, Babak Shahbaba

To address this issue, several methods based on surrogate models have been proposed to speed up the inference process.

Bayesian Inference Dimensionality Reduction

Optimal Experimental Design for Mathematical Models of Hematopoiesis

1 code implementation20 Apr 2020 Luis Martinez Lomeli, Abdon Iniguez, Babak Shahbaba, John S Lowengrub, Vladimir Minin

In this work, we aim to uncover the underlying mechanisms in hematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution.

Methodology Quantitative Methods Applications

Conjoined Dirichlet Process

1 code implementation8 Feb 2020 Michelle N. Ngo, Dustin S. Pluta, Alexander N. Ngo, Babak Shahbaba

Biclustering is a class of techniques that simultaneously clusters the rows and columns of a matrix to sort heterogeneous data into homogeneous blocks.

Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method

no code implementations13 Oct 2019 Tian Chen, Lingge Li, Gabriel Elias, Norbert Fortin, Babak Shahbaba

We show that our method leads to substantially higher accuracy rate for neural decoding and allows to discover novel biological phenomena by providing a clear latent representation of the decoding process.

Point Processes Representation Learning

Variational Hamiltonian Monte Carlo via Score Matching

no code implementations6 Feb 2016 Cheng Zhang, Babak Shahbaba, Hongkai Zhao

Traditionally, the field of computational Bayesian statistics has been divided into two main subfields: variational methods and Markov chain Monte Carlo (MCMC).

Bayesian Inference

Sampling constrained probability distributions using Spherical Augmentation

no code implementations19 Jun 2015 Shiwei Lan, Babak Shahbaba

In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space.

Bayesian Inference

Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases

1 code implementation18 Jun 2015 Cheng Zhang, Babak Shahbaba, Hongkai Zhao

To this end, we build a surrogate function to approximate the target distribution using properly chosen random bases and an efficient optimization process.

Additive models Bayesian Inference

Dependent Matérn Processes for Multivariate Time Series

no code implementations11 Feb 2015 Alexander Vandenberg-Rodes, Babak Shahbaba

For the challenging task of modeling multivariate time series, we propose a new class of models that use dependent Mat\'ern processes to capture the underlying structure of data, explain their interdependencies, and predict their unknown values.

Time Series

Split Hamiltonian Monte Carlo

no code implementations29 Jun 2011 Babak Shahbaba, Shiwei Lan, Wesley O. Johnson, Radford M. Neal

With the splitting technique, only the slowly-varying part of the energy needs to be handled numerically, and this can be done with a larger stepsize (and hence fewer steps) than would be necessary with a direct simulation of the dynamics.


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