Mijung Park
Mijung Park
Max Planck Institute for Intelligent Systems
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Title
Cited by
Cited by
Year
Receptive field inference with localized priors
M Park, JW Pillow
PLoS Comput Biol 7 (10), e1002219, 2011
682011
K2-ABC: Approximate Bayesian computation with kernel embeddings
M Park, W Jitkrittum, D Sejdinovic
AISTATS 2016 51, 2016
332016
DP-EM: Differentially Private Expectation Maximization
M Park, J Foulds, K Chaudhuri, M Welling
AISTATS 2017, 0
25*
Variational Bayes In Private Settings (VIPS)
M Park, J Foulds, K Chaudhuri, M Welling
JAIR 2020, to appear, 2016
232016
Bayesian active learning with localized priors for fast receptive field characterization
M Park, JW Pillow
Advances in neural information processing systems, 2348-2356, 2012
212012
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
202018
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
202015
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
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
182014
Adaptive Bayesian methods for closed-loop neurophysiology
JW Pillow, M Park
Closed loop neuroscience, 3-18, 2016
172016
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
162014
Bayesian inference for low rank spatiotemporal neural receptive fields
M Park, JW Pillow
Advances in Neural Information Processing Systems, 2688-2696, 2013
152013
Unlocking neural population non-stationarities using hierarchical dynamics models
M Park, G Bohner, JH Macke
Advances in Neural Information Processing Systems, 145-153, 2015
132015
Variational Bayesian inference for forecasting hierarchical time series
M Park, M Nassar
ICML Workshop 2014, 2014
132014
Bayesian active learning for drug combinations
M Park, M Nassar, H Vikalo
IEEE Transactions on Biomedical Engineering 60 (11), 3248-3255, 2013
132013
Private Topic Modeling
M Park, J Foulds, K Chaudhuri, M Welling
NeurIPS Workshop 2016, 2016
112016
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
Bayesian structure learning for functional neuroimaging
M Park, O Koyejo, J Ghosh, R Poldrack, J Pillow
Artificial Intelligence and Statistics, 489-497, 2013
72013
Radial and Directional Posteriors for Bayesian Neural Networks
C Oh, K Adamczewski, M Park
AAAI, 2020
42020
Interpretable and Differentially Private Predictions
F Harder, M Bauer, M Park
AAAI, 2020
42020
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Articles 1–20