Adam M. Johansen

Orcid: 0000-0002-3531-7628

According to our database1, Adam M. Johansen authored at least 23 papers between 2006 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2024
Solving Fredholm Integral Equations of the Second Kind via Wasserstein Gradient Flows.
CoRR, 2024

Fast convergence of the Expectation Maximization algorithm under a logarithmic Sobolev inequality.
CoRR, 2024

Particle Semi-Implicit Variational Inference.
CoRR, 2024

Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities.
CoRR, 2024

Momentum Particle Maximum Likelihood.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Cost free hyper-parameter selection/averaging for Bayesian inverse problems with vanilla and Rao-Blackwellized SMC samplers.
Stat. Comput., December, 2023

Divide-and-Conquer Fusion.
J. Mach. Learn. Res., 2023

Particle algorithms for maximum likelihood training of latent variable models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
The node-wise Pseudo-marginal method: model selection with spatial dependence on latent graphs.
Stat. Comput., 2022

Product-form estimators: exploiting independence to scale up Monte Carlo.
Stat. Comput., 2022

Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows.
CoRR, 2022

Scalable particle-based alternatives to EM.
CoRR, 2022

2021
A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells.
PLoS Comput. Biol., 2021

Global Consensus Monte Carlo.
J. Comput. Graph. Stat., 2021

2020
Generalized Bayesian Filtering via Sequential Monte Carlo.
CoRR, 2020

Generalised Bayesian Filtering via Sequential Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2017
Bayesian model comparison with un-normalised likelihoods.
Stat. Comput., 2017

2015
Convergence of the k-Means Minimization Problem using Γ-Convergence.
SIAM J. Appl. Math., 2015

2012
Bayesian model comparison via path-sampling sequential Monte Carlo.
Proceedings of the IEEE Statistical Signal Processing Workshop, 2012

2010
On solving integral equations using Markov chain Monte Carlo methods.
Appl. Math. Comput., 2010

2008
Markov Chains.
Proceedings of the Wiley Encyclopedia of Computer Science and Engineering, 2008

Particle methods for maximum likelihood estimation in latent variable models.
Stat. Comput., 2008

2006
Maximum Likelihood Parameter Estimation for Latent Variable Models Using Sequential Monte Carlo.
Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing, 2006


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