Mijung Park

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Jonathan PillowProfessor of Neuroscience, Princeton UniversityDirección de correo verificada de princeton.edu
Max WellingDistinguished Scientist at MSR AI4Science & Professor Machine Learning, University of AmsterdamDirección de correo verificada de uva.nl
Wittawat JitkrittumGoogle ResearchDirección de correo verificada de google.com
James FouldsUniversity of Maryland, Baltimore CountyDirección de correo verificada de umbc.edu
Dino SejdinovicProfessor, University of AdelaideDirección de correo verificada de adelaide.edu.au
Kamalika ChaudhuriProfessor of Computer Science, UCSD. Research Scientist, Meta AIDirección de correo verificada de ucsd.edu
Greg HorwitzUniversity of WashingtonDirección de correo verificada de u.washington.edu
Marcel NassarIntel AI LabDirección de correo verificada de utexas.edu
Haris VikaloProfessor, University of Texas at AustinDirección de correo verificada de ece.utexas.edu
Jakob MackeMachine Learning in Science, Tübingen UniversityDirección de correo verificada de uni-tuebingen.de
Changyong OhPhD student, University of AmsterdamDirección de correo verificada de uva.nl
Maneesh SahaniGatsby Unit, UCLDirección de correo verificada de gatsby.ucl.ac.uk
Lars BuesingGoogle DeepMindDirección de correo verificada de google.com
Anqi WuAssistant Professor, Computational Science and Engineering, Georgia TechDirección de correo verificada de gatech.edu
Robert HeathLampe Distinguished Professor, North Carolina State UniversityDirección de correo verificada de ncsu.edu
Anant RajINRIADirección de correo verificada de inria.fr
Sanmi KoyejoAssistant Professor, Stanford UniversityDirección de correo verificada de stanford.edu
Joydeep Ghosh(Chaired) Professor, ECE Dept., Univ. Texas at Austin; Faculty Dell Med, UT-Comp. Sc., McCombsDirección de correo verificada de utexas.edu
Russ PoldrackProfessor of Psychology, Stanford UniversityDirección de correo verificada de stanford.edu
Brian L. EvansEngineering Foundation Professor, ECE, WNCG, University of Texas at AustinDirección de correo verificada de ece.utexas.edu

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Mijung Park

Technical University of Denmark

Dirección de correo verificada de dtu.dk - Página principal

Machine learning Bayesian statistics differential privacy


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Receptive field inference with localized priors M Park, JW Pillow PLoS Comput Biol 7 (10), e1002219, 2011	101	2011
K2-ABC: Approximate Bayesian computation with kernel embeddings M Park, W Jitkrittum, D Sejdinovic AISTATS 2016 51, 2016	99	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	67*	2021
DP-EM: Differentially Private Expectation Maximization M Park, J Foulds, K Chaudhuri, M Welling AISTATS 2017, 2017	55	2017
Variational Bayes In Private Settings (VIPS) M Park, J Foulds, K Chaudhuri, M Welling JAIR 2020, 2016	54*	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	42	2018
Interpretable and Differentially Private Predictions F Harder, M Bauer, M Park AAAI, 2020	38	2020
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	29	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	28	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	25	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	25	2012
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
Variational Bayesian inference for forecasting hierarchical time series M Park, M Nassar ICML Workshop 2014, 2014	23	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	21	2014
Bayesian inference for low rank spatiotemporal neural receptive fields M Park, JW Pillow Advances in Neural Information Processing Systems, 2688-2696, 2013	19	2013
Pre-trained perceptual features improve differentially private image generation F Harder, M Jalali, DJ Sutherland, M Park Transactions on Machine Learning Research, 2023	16*	2023
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	16*	2020
Private Topic Modeling M Park, J Foulds, K Chaudhuri, M Welling NeurIPS Workshop 2016, 2016	15	2016
Unlocking neural population non-stationarities using hierarchical dynamics models M Park, G Bohner, JH Macke Advances in Neural Information Processing Systems, 145-153, 2015	13	2015

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