Mijung Park

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Jonathan PillowProfessor of Neuroscience, Princeton UniversityVerified email at princeton.edu
Max WellingCAIO CuspAI & Professor 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 of Statistical Machine Learning, University of AdelaideVerified email at adelaide.edu.au
Greg HorwitzUniversity of WashingtonVerified email at u.washington.edu
Marcel NassarAltos LabsVerified email at altoslabs.com
Haris VikaloProfessor, University of Texas at AustinVerified email at ece.utexas.edu
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
Jakob MackeMachine Learning in Science, Tübingen UniversityVerified email at uni-tuebingen.de
Robert HeathCharles Lee Powell Chair in Wireless Communication, University of California, San DiegoVerified email at ucsd.edu
Anant RajINRIAVerified email at inria.fr
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
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
K2-ABC: Approximate Bayesian computation with kernel embeddings M Park, W Jitkrittum, D Sejdinovic AISTATS 2016 51, 2016	109	2016
Receptive field inference with localized priors M Park, JW Pillow PLoS Comput Biol 7 (10), e1002219, 2011	104	2011
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	95	2021
DP-EM: Differentially Private Expectation Maximization M Park, J Foulds, K Chaudhuri, M Welling AISTATS 2017, 2017	61	2017
Variational Bayes In Private Settings (VIPS) M Park, J Foulds, K Chaudhuri, M Welling JAIR 2020, 2016	58*	2016
Interpretable and Differentially Private Predictions F Harder, M Bauer, M Park AAAI, 2020	53	2020
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	53	2018
Pre-trained perceptual features improve differentially private image generation F Harder, M Jalali, DJ Sutherland, M Park Transactions on Machine Learning Research, 2023	30*	2023
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	30	2011
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	29	2015
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	28	2014
Bayesian active learning with localized priors for fast receptive field characterization M Park, JW Pillow Advances in neural information processing systems, 2348-2356, 2012	27	2012
Variational Bayesian inference for forecasting hierarchical time series M Park, M Nassar ICML Workshop 2014, 2014	25	2014
Bayesian active learning for drug combinations M Park, M Nassar, H Vikalo IEEE Transactions on Biomedical Engineering 60 (11), 3248-3255, 2013	24	2013
Adaptive Bayesian methods for closed-loop neurophysiology JW Pillow, M Park Closed loop neuroscience, 3-18, 2016	23	2016
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	22	2014
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	21*	2020
Bayesian inference for low rank spatiotemporal neural receptive fields M Park, JW Pillow Advances in Neural Information Processing Systems, 2688-2696, 2013	19	2013
Differentially Private Latent Diffusion Models S Lyu, M Vinaroz, MF Liu, M Park arXiv preprint arXiv:2305.15759, 2023	18	2023
Unlocking neural population non-stationarities using hierarchical dynamics models M Park, G Bohner, JH Macke Advances in Neural Information Processing Systems, 145-153, 2015	18	2015

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