Statistics and Machine Learning
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Estimating the Unique Information of Continuous Variables
Ari Pakman, Amin Nejatbakhsh, Dar Gilboa, Abdullah Makkeh, Luca Mazzucato, Michael Wibral, Elad Schneidman
NeurIPS 2021
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A Bayesian nonparametric approach to super-resolution single-molecule localization
Mariano Gabitto, Herve Marie-Nelly, Ari Pakman, Andras Pataki, Xavier Darzacq, Michael Jordan
Annals of Applied Statistics 2021
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Neural Clustering Processes
Ari Pakman, Yueqi Wang, Catalin Mitelut, JinHyung Lee, and Liam Paninski
ICML 2020
Code: [Pytorch]
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Neural Permutation Processes
Ari Pakman, Yueqi Wang, and Liam Paninski
2nd Symposium on Advances in Approximate Bayesian Inference, Vancouver, 2019
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Amortized Bayesian Inference for Clustering Models
Ari Pakman and Liam Paninski
BNP@NeurIPS 2018 Workshop on Bayesian Nonparametrics (Spotlight Presentation)
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Binary Bouncy Particle Sampler
Ari Pakman
NIPS 2017 Workshop Advances in Approximate Bayesian Inference (Spotlight Presentation)
Code: [MATLAB/C++]
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Stochastic Bouncy Particle Sampler
Ari Pakman*, Dar Gilboa*, David Carlson, and Liam Paninski
ICML, 2017 [poster]
Code: [Tensorflow]
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Partition Functions from Rao-Blackwellized Tempered Sampling
David Carlson*, Patrick Stinson*, Ari Pakman*, and Liam Paninski
ICML, 2016 [poster]
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Taming the Noise in Reinforcement Learning via Soft Updates
Roy Fox*, Ari Pakman*, and Naftali Tishby
UAI, 2016, Plenary Presentation [poster]
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Auxiliary-variable exact Hamiltonian Monte Carlo samplers for binary distributions
Ari Pakman and Liam Paninski
NIPS, 2013 [poster]
Code: [MATLAB]
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Exact Hamiltonian Monte Carlo for Truncated Multivariate Gaussians
Ari Pakman and Liam Paninski
Journal of Computational and Graphical Statistics, Volume 23, Issue 2, 2014.
Code: [R package] [MATLAB]
