Mijung Park 个人学术档案 - 学术资源搜索

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Jonathan PillowProfessor of Neuroscience, Princeton University在 princeton.edu 的电子邮件经过验证
Max WellingDistinguished Scientist at MSR AI4Science & Professor Machine Learning, University of Amsterdam在 uva.nl 的电子邮件经过验证
Wittawat JitkrittumGoogle Research在 google.com 的电子邮件经过验证
James FouldsUniversity of Maryland, Baltimore County在 umbc.edu 的电子邮件经过验证
Dino SejdinovicProfessor of Statistical Machine Learning, University of Adelaide在 adelaide.edu.au 的电子邮件经过验证
Greg HorwitzUniversity of Washington在 u.washington.edu 的电子邮件经过验证
Marcel NassarAltos Labs在 altoslabs.com 的电子邮件经过验证
Haris VikaloProfessor, University of Texas at Austin在 ece.utexas.edu 的电子邮件经过验证
Changyong OhPhD student, University of Amsterdam在 uva.nl 的电子邮件经过验证
Maneesh SahaniGatsby Unit, UCL在 gatsby.ucl.ac.uk 的电子邮件经过验证
Lars BuesingGoogle DeepMind在 google.com 的电子邮件经过验证
Anqi WuAssistant Professor, Computational Science and Engineering, Georgia Tech在 gatech.edu 的电子邮件经过验证
Jakob MackeMachine Learning in Science, Tübingen University在 uni-tuebingen.de 的电子邮件经过验证
Robert HeathCharles Lee Powell Chair in Wireless Communication, University of California, San Diego在 ucsd.edu 的电子邮件经过验证
Anant RajINRIA在 inria.fr 的电子邮件经过验证
Sanmi KoyejoAssistant Professor, Stanford University在 stanford.edu 的电子邮件经过验证
Joydeep Ghosh(Chaired) Professor, ECE Dept., Univ. Texas at Austin; Faculty Dell Med, UT-Comp. Sc., McCombs在 utexas.edu 的电子邮件经过验证
Russ PoldrackProfessor of Psychology, Stanford University在 stanford.edu 的电子邮件经过验证
Brian L. EvansEngineering Foundation Professor, ECE, WNCG, University of Texas at Austin在 ece.utexas.edu 的电子邮件经过验证

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Mijung Park

Technical University of Denmark

在 dtu.dk 的电子邮件经过验证 - 首页

Machine learning Bayesian statistics differential privacy


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
K2-ABC: Approximate Bayesian computation with kernel embeddings M Park, W Jitkrittum, D Sejdinovic AISTATS 2016 51, 2016	106	2016
Receptive field inference with localized priors M Park, JW Pillow PLoS Comput Biol 7 (10), e1002219, 2011	102	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	86	2021
DP-EM: Differentially Private Expectation Maximization M Park, J Foulds, K Chaudhuri, M Welling AISTATS 2017, 2017	59	2017
Variational Bayes In Private Settings (VIPS) M Park, J Foulds, K Chaudhuri, M Welling JAIR 2020, 2016	55*	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	50	2018
Interpretable and Differentially Private Predictions F Harder, M Bauer, M Park AAAI, 2020	49	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	30	2011
Pre-trained perceptual features improve differentially private image generation F Harder, M Jalali, DJ Sutherland, M Park Transactions on Machine Learning Research, 2023	28*	2023
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 with localized priors for fast receptive field characterization M Park, JW Pillow Advances in neural information processing systems, 2348-2356, 2012	27	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	26	2014
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
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
Adaptive Bayesian methods for closed-loop neurophysiology JW Pillow, M Park Closed loop neuroscience, 3-18, 2016	21	2016
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	20*	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
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
Hermite Polynomial Features for Private Data Generation M Vinaroz, MA Charusaie, F Harder, K Adamczewski, M Park ICML 2022, arXiv: 2106.05042, 2021	16	2021

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