Mijung Park

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Jonathan PillowProfessor of Neuroscience, Princeton UniversityVerified email at princeton.edu
Max WellingProfessor Machine Learning, University of AmsterdamVerified email at uva.nl
Wittawat JitkrittumGoogle ResearchVerified email at google.com
James FouldsUniversity of Maryland, Baltimore CountyVerified email at umbc.edu
Dino SejdinovicProfessor, University of AdelaideVerified email at adelaide.edu.au
Kamalika ChaudhuriProfessor of Computer Science, UCSD. Research Scientist, Meta AIVerified email at ucsd.edu
Greg HorwitzUniversity of WashingtonVerified email at u.washington.edu
Marcel NassarIntel AI LabVerified email at utexas.edu
Haris VikaloProfessor, University of Texas at AustinVerified email at ece.utexas.edu
Jakob MackeMachine Learning in Science, Tübingen UniversityVerified email at uni-tuebingen.de
Changyong OhPhD student, University of AmsterdamVerified email at uva.nl
Maneesh SahaniGatsby Unit, UCLVerified email at gatsby.ucl.ac.uk
Lars BuesingGoogle DeepMindVerified email at google.com
Anqi WuAssistant Professor, Computational Science and Engineering, Georgia TechVerified email at gatech.edu
Robert HeathLampe Distinguished Professor, North Carolina State UniversityVerified email at ncsu.edu
Sanmi KoyejoAssistant Professor, Stanford UniversityVerified email at stanford.edu
Joydeep Ghosh(Chaired) Professor, ECE Dept., Univ. Texas at Austin; Faculty Dell Med, UT-Comp. Sc., McCombsVerified email at utexas.edu
Russ PoldrackProfessor of Psychology, Stanford UniversityVerified email at stanford.edu
Anant RajINRIAVerified email at inria.fr
Brian L. EvansEngineering Foundation Professor, ECE, WNCG, University of Texas at AustinVerified email at ece.utexas.edu

Mijung Park

University of British Columbia

Verified email at cs.ubc.ca - Homepage

Machine learning Bayesian statistics differential privacy


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Receptive field inference with localized priors M Park, JW Pillow PLoS Comput Biol 7 (10), e1002219, 2011	94	2011
K2-ABC: Approximate Bayesian computation with kernel embeddings M Park, W Jitkrittum, D Sejdinovic AISTATS 2016 51, 2016	87	2016
DP-EM: Differentially Private Expectation Maximization M Park, J Foulds, K Chaudhuri, M Welling AISTATS 2017, 2017	47	2017
Variational Bayes In Private Settings (VIPS) M Park, J Foulds, K Chaudhuri, M Welling JAIR 2020, 2016	47*	2016
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	38	2018
DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation F Harder, K Adamczewski, M Park International Conference on Artificial Intelligence and Statistics, 1819-1827, 2021	36*	2021
Interpretable and Differentially Private Predictions F Harder, M Bauer, M Park AAAI, 2020	28	2020
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	27	2015
Active learning of neural response functions with Gaussian processes M Park, G Horwitz, J Pillow Advances in neural information processing systems 24, 2043-2051, 2011	27	2011
Bayesian active learning with localized priors for fast receptive field characterization M Park, JW Pillow Advances in neural information processing systems, 2348-2356, 2012	25	2012
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	24	2014
Bayesian active learning for drug combinations M Park, M Nassar, H Vikalo IEEE Transactions on Biomedical Engineering 60 (11), 3248-3255, 2013	23	2013
Adaptive Bayesian methods for closed-loop neurophysiology JW Pillow, M Park Closed loop neuroscience, 3-18, 2016	22	2016
Variational Bayesian inference for forecasting hierarchical time series M Park, M Nassar ICML Workshop 2014, 2014	21	2014
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	19	2014
Bayesian inference for low rank spatiotemporal neural receptive fields M Park, JW Pillow Advances in Neural Information Processing Systems, 2688-2696, 2013	16	2013
Radial and Directional Posteriors for Bayesian Deep Learning C Oh, K Adamczewski, M Park Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5298-5305, 2020	15*	2020
Private Topic Modeling M Park, J Foulds, K Chaudhuri, M Welling NeurIPS Workshop 2016, 2016	14	2016
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	13	2011
Unlocking neural population non-stationarities using hierarchical dynamics models M Park, G Bohner, JH Macke Advances in Neural Information Processing Systems, 145-153, 2015	12	2015

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