Ted Meeds

Immunosequencing of the T-cell receptor repertoire reveals signatures specific for diagnosis and characterization of early Lyme disease

Julia Greissl, Mitch Pesesky, Sudeb C. Dalai, Alison W. Rebman, Mark J. Soloski, Elizabeth J. Horn, Jennifer N. Dines, Rachel M. Gittelman, Thomas M. Snyder, Ryan O. Emerson, Ted Meeds, Thomas Manley, Ian M. Kaplan, Lance Baldo, Jonathan M. Carlson, Harlan S. Robins, John N. Aucott

medRxiv | August 2021

DOI PDF Publication Project

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems

Geoffrey Roeder, Paul Grant, Andrew Phillips, Neil Dalchau, Ted Meeds

ICML 2019 | June 2019

Author's Version PDF Project Project Project

Deterministic Variational Inference for Robust Bayesian Neural Networks

Anqi Wu, Sebastian Nowozin, Ted Meeds (edmeeds), Richard E. Turner, Jose Miguel Hernadez-Lobato, Alexander L. Gaunt

International Conference on Learning Representations | May 2019

Publication

Soft Weight-Sharing for Neural Network Compression

Karen Ullrich, Max Welling, Ted Meeds

International Conference on Representation Learning (ICLR) | July 2017

Publication

Automatic Variational ABC

Alexander Moreno, Tameem Adel, Ted Meeds, James M. Rehg, Max Welling

June 2016

Publication Publication

Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference

Ted Meeds, Max Welling

Neural Information Processing Systems (NIPS) | December 2015

Publication

POPE: post optimization posterior evaluation of likelihood free models

Ted Meeds, Michael Chiang, Mary Lee, Olivier Cinquin, John Lowengrub, Max Welling

BMC Bioinformatics | August 2015, Vol 26

Publication Publication Publication

MLitB: machine learning in the browser

Ted Meeds, Remco Hendriks, Said Al Farady, Magiel Bruntink, Max Welling

PeerJ Computer Science | July 2015, Vol e11

Publication Publication

Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation

Michael Chiang, Amanda Cinquin, Adrian Paz, Ted Meeds (edmeeds), Christopher A. Price, Max Welling, Olivier Cinquin

BMC Biology | July 2015, Vol 13

Publication Publication

Hamiltonian ABC

Ted Meeds, Robert Leenders, Max Welling

Uncertainty in Artificial Intelligence (UAI) | July 2015

Publication

GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation

Ted Meeds, Max Welling

Uncertainty in Artificial Intelligence | July 2014

Publication

Learning stick-figure models using nonparametric Bayesian priors over trees

Ted Meeds, David A. Ross, Richard Zemel, Sam Roweis

Conference on Computer Vision and Pattern Recognition (CVPR) | August 2008

Publication

Nonparametric Bayesian Biclustering

Ted Meeds, Sam Roweis

UTML TR 2007–001 | January 2007

Publication

Modeling Dyadic Data with Binary Latent Factors

Ted Meeds, Zoubin Ghahramani, Radford Neal, Sam Roweis

Neural Information Processing Systems | December 2006

Publication

An Alternative Infinite Mixture Of Gaussian Process Experts

Ted Meeds, Simon Osindero

Neural Information Processing Systems | December 2005

Publication

Novelty Detection Model Selection Using Volume Estimation

Ted Meeds

UTML TR 2005–004 | August 2005

Publication

Immunosequencing of the T-cell receptor repertoire reveals signatures specific for diagnosis and characterization of early Lyme disease

Julia Greissl, Mitch Pesesky, Sudeb C. Dalai, Alison W. Rebman, Mark J. Soloski, Elizabeth J. Horn, Jennifer N. Dines, Rachel M. Gittelman, Thomas M. Snyder, Ryan O. Emerson, Ted Meeds, Thomas Manley, Ian M. Kaplan, Lance Baldo, Jonathan M. Carlson, Harlan S. Robins, John N. Aucott

medRxiv | August 2021

DOI PDF Publication Project

Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation

Michael Chiang, Amanda Cinquin, Adrian Paz, Ted Meeds (edmeeds), Christopher A. Price, Max Welling, Olivier Cinquin

BMC Biology | July 2015, Vol 13

Publication Publication

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems

Geoffrey Roeder, Paul Grant, Andrew Phillips, Neil Dalchau, Ted Meeds

ICML 2019 | June 2019

Author's Version PDF Project Project Project

Deterministic Variational Inference for Robust Bayesian Neural Networks

Anqi Wu, Sebastian Nowozin, Ted Meeds (edmeeds), Richard E. Turner, Jose Miguel Hernadez-Lobato, Alexander L. Gaunt

International Conference on Learning Representations | May 2019

Publication

Soft Weight-Sharing for Neural Network Compression

Karen Ullrich, Max Welling, Ted Meeds

International Conference on Representation Learning (ICLR) | July 2017

Publication

Automatic Variational ABC

Alexander Moreno, Tameem Adel, Ted Meeds, James M. Rehg, Max Welling

June 2016

Publication Publication

Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference

Ted Meeds, Max Welling

Neural Information Processing Systems (NIPS) | December 2015

Publication

POPE: post optimization posterior evaluation of likelihood free models

Ted Meeds, Michael Chiang, Mary Lee, Olivier Cinquin, John Lowengrub, Max Welling

BMC Bioinformatics | August 2015, Vol 26

Publication Publication Publication

MLitB: machine learning in the browser

Ted Meeds, Remco Hendriks, Said Al Farady, Magiel Bruntink, Max Welling

PeerJ Computer Science | July 2015, Vol e11

Publication Publication

Hamiltonian ABC

Ted Meeds, Robert Leenders, Max Welling

Uncertainty in Artificial Intelligence (UAI) | July 2015

Publication

GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation

Ted Meeds, Max Welling

Uncertainty in Artificial Intelligence | July 2014

Publication

Nonparametric Bayesian Biclustering

Ted Meeds, Sam Roweis

UTML TR 2007–001 | January 2007

Publication

Modeling Dyadic Data with Binary Latent Factors

Ted Meeds, Zoubin Ghahramani, Radford Neal, Sam Roweis

Neural Information Processing Systems | December 2006

Publication

An Alternative Infinite Mixture Of Gaussian Process Experts

Ted Meeds, Simon Osindero

Neural Information Processing Systems | December 2005

Publication

Novelty Detection Model Selection Using Volume Estimation

Ted Meeds

UTML TR 2005–004 | August 2005

Publication

MLitB: machine learning in the browser

Ted Meeds, Remco Hendriks, Said Al Farady, Magiel Bruntink, Max Welling

PeerJ Computer Science | July 2015, Vol e11

Publication Publication

Learning stick-figure models using nonparametric Bayesian priors over trees

Ted Meeds, David A. Ross, Richard Zemel, Sam Roweis

Conference on Computer Vision and Pattern Recognition (CVPR) | August 2008

Publication

Immunosequencing of the T-cell receptor repertoire reveals signatures specific for diagnosis and characterization of early Lyme disease

Julia Greissl, Mitch Pesesky, Sudeb C. Dalai, Alison W. Rebman, Mark J. Soloski, Elizabeth J. Horn, Jennifer N. Dines, Rachel M. Gittelman, Thomas M. Snyder, Ryan O. Emerson, Ted Meeds, Thomas Manley, Ian M. Kaplan, Lance Baldo, Jonathan M. Carlson, Harlan S. Robins, John N. Aucott

medRxiv | August 2021

DOI PDF Publication Project

POPE: post optimization posterior evaluation of likelihood free models

Ted Meeds, Michael Chiang, Mary Lee, Olivier Cinquin, John Lowengrub, Max Welling

BMC Bioinformatics | August 2015, Vol 26

Publication Publication Publication

MLitB: machine learning in the browser

Ted Meeds, Remco Hendriks, Said Al Farady, Magiel Bruntink, Max Welling

PeerJ Computer Science | July 2015, Vol e11

Publication Publication

Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation

Michael Chiang, Amanda Cinquin, Adrian Paz, Ted Meeds (edmeeds), Christopher A. Price, Max Welling, Olivier Cinquin

BMC Biology | July 2015, Vol 13

Publication Publication

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems

Geoffrey Roeder, Paul Grant, Andrew Phillips, Neil Dalchau, Ted Meeds

ICML 2019 | June 2019

Author's Version PDF Project Project Project

Deterministic Variational Inference for Robust Bayesian Neural Networks

Anqi Wu, Sebastian Nowozin, Ted Meeds (edmeeds), Richard E. Turner, Jose Miguel Hernadez-Lobato, Alexander L. Gaunt

International Conference on Learning Representations | May 2019

Publication

Soft Weight-Sharing for Neural Network Compression

Karen Ullrich, Max Welling, Ted Meeds

International Conference on Representation Learning (ICLR) | July 2017

Publication

Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference

Ted Meeds, Max Welling

Neural Information Processing Systems (NIPS) | December 2015

Publication

Hamiltonian ABC

Ted Meeds, Robert Leenders, Max Welling

Uncertainty in Artificial Intelligence (UAI) | July 2015

Publication

GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation

Ted Meeds, Max Welling

Uncertainty in Artificial Intelligence | July 2014

Publication

Learning stick-figure models using nonparametric Bayesian priors over trees

Ted Meeds, David A. Ross, Richard Zemel, Sam Roweis

Conference on Computer Vision and Pattern Recognition (CVPR) | August 2008

Publication

Modeling Dyadic Data with Binary Latent Factors

Ted Meeds, Zoubin Ghahramani, Radford Neal, Sam Roweis

Neural Information Processing Systems | December 2006

Publication

An Alternative Infinite Mixture Of Gaussian Process Experts

Ted Meeds, Simon Osindero

Neural Information Processing Systems | December 2005

Publication

Automatic Variational ABC

Alexander Moreno, Tameem Adel, Ted Meeds, James M. Rehg, Max Welling

June 2016

Publication Publication

Nonparametric Bayesian Biclustering

Ted Meeds, Sam Roweis

UTML TR 2007–001 | January 2007

Publication

Novelty Detection Model Selection Using Volume Estimation

Ted Meeds

UTML TR 2005–004 | August 2005

Publication