Mijung Park

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Jonathan PillowProfessor of Neuroscience, Princeton UniversityEmail verificata su princeton.edu
Max WellingCAIO CuspAI & Professor Machine Learning, University of AmsterdamEmail verificata su uva.nl
Wittawat JitkrittumGoogle ResearchEmail verificata su google.com
James FouldsUniversity of Maryland, Baltimore CountyEmail verificata su umbc.edu
Dino SejdinovicProfessor of Statistical Machine Learning, University of AdelaideEmail verificata su adelaide.edu.au
Greg HorwitzUniversity of WashingtonEmail verificata su u.washington.edu
Marcel NassarAltos LabsEmail verificata su altoslabs.com
Haris VikaloProfessor, University of Texas at AustinEmail verificata su ece.utexas.edu
Changyong OhPhD student, University of AmsterdamEmail verificata su uva.nl
Maneesh SahaniGatsby Unit, UCLEmail verificata su gatsby.ucl.ac.uk
Lars BuesingGoogle DeepMindEmail verificata su google.com
Anqi WuAssistant Professor, Computational Science and Engineering, Georgia TechEmail verificata su gatech.edu
Jakob MackeMachine Learning in Science, Tübingen UniversityEmail verificata su uni-tuebingen.de
Robert HeathCharles Lee Powell Chair in Wireless Communication, University of California, San DiegoEmail verificata su ucsd.edu
Anant RajAssistant Professor, Indian Institute of Science, BengaluruEmail verificata su iisc.ac.in
Sanmi KoyejoAssistant Professor, Stanford UniversityEmail verificata su stanford.edu
Joydeep Ghosh(Chaired) Professor, ECE Dept., Univ. Texas at Austin; Faculty Dell Med, UT-Comp. Sc., McCombsEmail verificata su utexas.edu
Russ PoldrackProfessor of Psychology, Stanford UniversityEmail verificata su stanford.edu
Brian L. EvansEngineering Foundation Professor, ECE, WNCG, University of Texas at AustinEmail verificata su ece.utexas.edu

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Mijung Park

University of British Columbia

Email verificata su cs.ubc.ca - Home page

Machine learning Bayesian statistics differential privacy


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
K2-ABC: Approximate Bayesian computation with kernel embeddings M Park, W Jitkrittum, D Sejdinovic AISTATS 2016 51, 2016	112	2016
Receptive field inference with localized priors M Park, JW Pillow PLoS Comput Biol 7 (10), e1002219, 2011	105	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	104	2021
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	58	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	32*	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
Adaptive Bayesian methods for closed-loop neurophysiology JW Pillow, M Park Closed loop neuroscience, 3-18, 2016	24	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
Hermite Polynomial Features for Private Data Generation M Vinaroz, MA Charusaie, F Harder, K Adamczewski, M Park ICML 2022, arXiv: 2106.05042, 2021	23	2021
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
Differentially Private Latent Diffusion Models S Lyu, M Vinaroz, MF Liu, M Park Transactions on Machine Learning Research (TMLR) 2024, 2023	20	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	19	2015

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