Mijung Park

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Jonathan PillowProfessor of Neuroscience, Princeton UniversityAdresse e-mail validée de princeton.edu
Max WellingCAIO CuspAI & Professor Machine Learning, University of AmsterdamAdresse e-mail validée de uva.nl
Wittawat JitkrittumGoogle ResearchAdresse e-mail validée de google.com
James FouldsUniversity of Maryland, Baltimore CountyAdresse e-mail validée de umbc.edu
Dino SejdinovicProfessor of Statistical Machine Learning, University of AdelaideAdresse e-mail validée de adelaide.edu.au
Greg HorwitzUniversity of WashingtonAdresse e-mail validée de u.washington.edu
Marcel NassarAltos LabsAdresse e-mail validée de altoslabs.com
Haris VikaloProfessor, University of Texas at AustinAdresse e-mail validée de ece.utexas.edu
Changyong OhPhD student, University of AmsterdamAdresse e-mail validée de uva.nl
Maneesh SahaniGatsby Unit, UCLAdresse e-mail validée de gatsby.ucl.ac.uk
Lars BuesingGoogle DeepMindAdresse e-mail validée de google.com
Anqi WuAssistant Professor, Computational Science and Engineering, Georgia TechAdresse e-mail validée de gatech.edu
Jakob MackeMachine Learning in Science, Tübingen UniversityAdresse e-mail validée de uni-tuebingen.de
Robert HeathCharles Lee Powell Chair in Wireless Communication, University of California, San DiegoAdresse e-mail validée de ucsd.edu
Anant RajAssistant Professor, Indian Institute of Science, BengaluruAdresse e-mail validée de iisc.ac.in
Sanmi KoyejoAssistant Professor, Stanford UniversityAdresse e-mail validée de stanford.edu
Joydeep Ghosh(Chaired) Professor, ECE Dept., Univ. Texas at Austin; Faculty Dell Med, UT-Comp. Sc., McCombsAdresse e-mail validée de utexas.edu
Russ PoldrackProfessor of Psychology, Stanford UniversityAdresse e-mail validée de stanford.edu
Brian L. EvansEngineering Foundation Professor, ECE, WNCG, University of Texas at AustinAdresse e-mail validée de ece.utexas.edu

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Mijung Park

University of British Columbia

Adresse e-mail validée de cs.ubc.ca - Page d'accueil

Machine learning Bayesian statistics differential privacy


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
K2-ABC: Approximate Bayesian computation with kernel embeddings M Park, W Jitkrittum, D Sejdinovic AISTATS 2016 51, 2016	117	2016
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	111	2021
Receptive field inference with localized priors M Park, JW Pillow PLoS Comput Biol 7 (10), e1002219, 2011	110	2011
DP-EM: Differentially Private Expectation Maximization M Park, J Foulds, K Chaudhuri, M Welling AISTATS 2017, 2017	61	2017
Interpretable and Differentially Private Predictions F Harder, M Bauer, M Park AAAI, 2020	60	2020
Variational Bayes In Private Settings (VIPS) M Park, J Foulds, K Chaudhuri, M Welling JAIR 2020, 2016	58*	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	54	2018
Pre-trained perceptual features improve differentially private image generation F Harder, M Jalali, DJ Sutherland, M Park Transactions on Machine Learning Research, 2023	36*	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	32	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	30	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	26	2014
Hermite Polynomial Features for Private Data Generation M Vinaroz, MA Charusaie, F Harder, K Adamczewski, M Park ICML 2022, arXiv: 2106.05042, 2021	25	2021
Adaptive Bayesian methods for closed-loop neurophysiology JW Pillow, M Park Closed loop neuroscience, 3-18, 2016	25	2016
Bayesian active learning for drug combinations M Park, M Nassar, H Vikalo IEEE Transactions on Biomedical Engineering 60 (11), 3248-3255, 2013	24	2013
Differentially Private Latent Diffusion Models S Lyu, M Vinaroz, MF Liu, M Park Transactions on Machine Learning Research (TMLR) 2024, 2023	23	2023
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	23	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	22*	2020
Bayesian inference for low rank spatiotemporal neural receptive fields M Park, JW Pillow Advances in Neural Information Processing Systems, 2688-2696, 2013	20	2013

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