Babak Shahbaba

According to our database, Babak Shahbaba authored at least 25 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Weakly-Supervised Multimodal Learning on MIMIC-CXR.

Andrea Agostini, Daphné Chopard, Yang Meng, Norbert Fortin, Babak Shahbaba, Stephan Mandt, Thomas M. Sutter, Julia E. Vogt

CoRR, 2024

Optimal Transport for Latent Integration with An Application to Heterogeneous Neuronal Activity Data.

Yubai Yuan, Babak Shahbaba, Norbert Fortin, Keiland W. Cooper, Qing Nie, Annie Qu

CoRR, 2024

2023

A Model-Agnostic Graph Neural Network for Integrating Local and Global Information.

Wenzhuo Zhou, Annie Qu, Keiland W. Cooper, Norbert Fortin, Babak Shahbaba

CoRR, 2023

Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes.

Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone

Proceedings of the International Conference on Machine Learning, 2023

2022

Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks.

Shiwei Lan, Shuyi Li, Babak Shahbaba

SIAM/ASA J. Uncertain. Quantification, December, 2022

Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events.

Babak Shahbaba, Lingge Li, Forest Agostinelli, Mansi Saraf, Keiland W. Cooper, Derenik Haghverdian, Gabriel A. Elias, Pierre Baldi, Norbert J. Fortin

Dataset, February, 2022

Brain waves analysis via a non-parametric Bayesian mixture of autoregressive kernels.

Guilllermo Granados-Garcia, Mark Fiecas, Babak Shahbaba, Norbert Fortin, Hernando C. Ombao

Comput. Stat. Data Anal., 2022

2020

Conjoined Dirichlet Process.

Michelle N. Ngo, Dustin S. Pluta, Alexander N. Ngo, Babak Shahbaba

CoRR, 2020

Nonparametric Fisher Geometry with Application to Density Estimation.

Andrew Holbrook, Shiwei Lan, Jeffrey Streets, Babak Shahbaba

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

2019

Neural network gradient Hamiltonian Monte Carlo.

Lingge Li, Andrew Holbrook, Babak Shahbaba, Pierre Baldi

Comput. Stat., 2019

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

Tian Chen, Lingge Li, Gabriel Elias, Norbert Fortin, Babak Shahbaba

CoRR, 2019

Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes.

Lingge Li, Dustin S. Pluta, Babak Shahbaba, Norbert Fortin, Hernando Ombao, Pierre Baldi

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States.

Xu Gao, Babak Shahbaba, Hernando C. Ombao

J. Classif., 2018

2017

Hamiltonian Monte Carlo acceleration using surrogate functions with random bases.

Cheng Zhang, Babak Shahbaba, Hongkai Zhao

Stat. Comput., 2017

Precomputing strategy for Hamiltonian Monte Carlo method based on regularity in parameter space.

Cheng Zhang, Babak Shahbaba, Hongkai Zhao

Comput. Stat., 2017

2016

What time is it? Deep learning approaches for circadian rhythms.

Forest Agostinelli, Nicholas Ceglia, Babak Shahbaba, Paolo Sassone-Corsi, Pierre Baldi

Bioinform., 2016

2015

An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics.

Shiwei Lan, Julia A. Palacios, Michael D. Karcher, Vladimir N. Minin, Babak Shahbaba

Bioinform., 2015

2014

Split Hamiltonian Monte Carlo.

Babak Shahbaba, Shiwei Lan, Wesley O. Johnson, Radford M. Neal

Stat. Comput., 2014

A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons.

Babak Shahbaba, Bo Zhou, Shiwei Lan, Hernando Ombao, David Moorman, Sam Behseta

Neural Comput., 2014

Spherical Hamiltonian Monte Carlo for Constrained Target Distributions.

Shiwei Lan, Bo Zhou, Babak Shahbaba

Proceedings of the 31th International Conference on Machine Learning, 2014

Distributed Stochastic Gradient MCMC.

Sungjin Ahn, Babak Shahbaba, Max Welling

Proceedings of the 31th International Conference on Machine Learning, 2014

Wormhole Hamiltonian Monte Carlo.

Shiwei Lan, Jeffrey Streets, Babak Shahbaba

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2009

Nonlinear Models Using Dirichlet Process Mixtures.

Babak Shahbaba, Radford M. Neal

J. Mach. Learn. Res., 2009

2008

A modified Dirichlet process mixture model for clustering phosphopeptides based on their response to anti-cancer drug perturbation.

Babak Shahbaba, Eva Prenosil, Anjali Jain, Sylvia K. Plevritis

Proceedings of the International Conference on Bioinformatics & Computational Biology, 2008

2006

Gene function classification using Bayesian models with hierarchy-based priors.

Babak Shahbaba, Radford M. Neal

BMC Bioinform., 2006