Mijung Park
Mijung Park
Max Planck Institute for Intelligent Systems
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TitleCited byYear
Receptive field inference with localized priors
M Park, JW Pillow
PLoS Comput Biol 7 (10), e1002219, 2011
652011
K2-ABC: Approximate Bayesian computation with kernel embeddings
M Park, W Jitkrittum, D Sejdinovic
Proceedings of Machine Learning Research, 2016
322016
Bayesian active learning with localized priors for fast receptive field characterization
M Park, JW Pillow
Advances in neural information processing systems, 2348-2356, 2012
202012
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
M Park, W Jitkrittum, A Qamar, Z Szabó, L Buesing, M Sahani
Advances in Neural Information Processing Systems, 154-162, 2015
192015
Active learning of neural response functions with Gaussian processes
M Park, G Horwitz, JW Pillow
Advances in neural information processing systems, 2043-2051, 2011
192011
DP-EM: Differentially Private Expectation Maximization
M Park, J Foulds, K Chaudhuri, M Welling
AISTATS, 0
19*
Dethroning the Fano Factor: a flexible, model-based approach to partitioning neural variability
AS Charles, M Park, JP Weller, GD Horwitz, JW Pillow
Neural computation 30 (4), 1012-1045, 2018
172018
Adaptive Bayesian methods for closed-loop neurophysiology
JW Pillow, M Park
Closed loop neuroscience, 3-18, 2016
172016
Variational Bayes In Private Settings (VIPS)
M Park, J Foulds, K Chaudhuri, M Welling
arXiv preprint arXiv:1611.00340, JAIR 2020, 2016
152016
Bayesian active learning of neural firing rate maps with transformed gaussian process priors
M Park, JP Weller, GD Horwitz, JW Pillow
Neural computation 26 (8), 1519-1541, 2014
152014
Sparse Bayesian structure learning with “dependent relevance determination” priors
A Wu, M Park, OO Koyejo, JW Pillow
Advances in Neural Information Processing Systems, 1628-1636, 2014
152014
Bayesian inference for low rank spatiotemporal neural receptive fields
M Park, JW Pillow
Advances in Neural Information Processing Systems, 2688-2696, 2013
132013
Unlocking neural population non-stationarities using hierarchical dynamics models
M Park, G Bohner, JH Macke
Advances in Neural Information Processing Systems, 145-153, 2015
122015
Bayesian active learning for drug combinations
M Park, M Nassar, H Vikalo
IEEE Transactions on Biomedical Engineering 60 (11), 3248-3255, 2013
122013
Private Topic Modeling
M Park, J Foulds, K Chaudhuri, M Welling
arXiv preprint arXiv:1609.04120, 2016
102016
A machine learning approach to link adaptation for SC-FDE system
Z Puljiz, M Park, R Heath Jr
2011 IEEE Global Telecommunications Conference-GLOBECOM 2011, 1-5, 2011
92011
Variational Bayesian inference for forecasting hierarchical time series
M Park, M Nassar
ICML Workshop, 2014
82014
Bayesian structure learning for functional neuroimaging
M Park, O Koyejo, J Ghosh, R Poldrack, J Pillow
Artificial Intelligence and Statistics, 489-497, 2013
72013
Adaptive experimental design for drug combinations
M Park, M Nassar, BL Evans, H Vikalo
2012 IEEE Statistical Signal Processing Workshop (SSP), 712-715, 2012
42012
Radial and Directional Posteriors for Bayesian Neural Networks
C Oh, K Adamczewski, M Park
AAAI, 2020
22020
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Articles 1–20