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ORCIDMijung Park
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- affiliation: Max Planck Institute for Intelligent Systems, Tübingen, Germany
- affiliation: University of Tübingen, , Germany
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2020 – today
- 2024
[j10]Margarita Vinaroz, Mijung Park:
Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation. Trans. Mach. Learn. Res. 2024 (2024)- 2023
- [j9]Jyu-Lin Chen
, Chen-Xi Lin, Mijung Park, Jerry John Nutor, Rosalind de Lisser, Thomas J. Hoffmann, Hannah J. Kim:
Rapid response nursing triage outcomes for COVID-19: factors associated with patient's participation in triage recommendations. BMC Medical Informatics Decis. Mak. 23(1): 47 (2023) - [j8]Frederik Harder, Milad Jalali, Danica J. Sutherland, Mijung Park:
Pre-trained Perceptual Features Improve Differentially Private Image Generation. Trans. Mach. Learn. Res. 2023 (2023) - [i24]Margarita Vinaroz, Mijung Park:
Differentially Private Kernel Inducing Points (DP-KIP) for Privacy-preserving Data Distillation. CoRR abs/2301.13389 (2023) - [i23]Yilin Yang, Kamil Adamczewski, Danica J. Sutherland, Xiaoxiao Li, Mijung Park:
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation. CoRR abs/2303.01687 (2023) - [i22]Kamil Adamczewski, Mijung Park:
Differential Privacy Meets Neural Network Pruning. CoRR abs/2303.04612 (2023) - [i21]Saiyue Lyu, Margarita Vinaroz, Michael F. Liu, Mijung Park:
Differentially Private Latent Diffusion Models. CoRR abs/2305.15759 (2023) - [i20]Kamil Adamczewski, Yingchen He, Mijung Park:
Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification. CoRR abs/2306.11754 (2023) - 2022
- [j7]Margarita Vinaroz, Mijung Park:
Differentially Private Stochastic Expectation Propagation. Trans. Mach. Learn. Res. 2022 (2022) - [c18]Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mijung Park:
Hermite Polynomial Features for Private Data Generation. ICML 2022: 22300-22324 - [i19]Frederik Harder, Milad Jalali Asadabadi, Danica J. Sutherland, Mijung Park:
Differentially Private Data Generation Needs Better Features. CoRR abs/2205.12900 (2022) - 2021
- [j6]Mijung Park, Margarita Vinaroz, Wittawat Jitkrittum:
ABCDP: Approximate Bayesian Computation with Differential Privacy. Entropy 23(8): 961 (2021) - [c17]Frederik Harder, Kamil Adamczewski, Mijung Park:
DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation. AISTATS 2021: 1819-1827 - [c16]Kamil Adamczewski, Mijung Park:
Dirichlet Pruning for Convolutional Neural Networks. AISTATS 2021: 3637-3645 - [i18]Mijung Park, Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder:
Polynomial magic! Hermite polynomials for private data generation. CoRR abs/2106.05042 (2021) - [i17]Margarita Vinaroz, Mijung Park:
DP-SEP! Differentially Private Stochastic Expectation Propagation. CoRR abs/2111.13219 (2021) - 2020
- [j5]Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling:
Variational Bayes In Private Settings (VIPS). J. Artif. Intell. Res. 68: 109-157 (2020) - [c15]Frederik Harder, Matthias Bauer, Mijung Park:
Interpretable and Differentially Private Predictions. AAAI 2020: 4083-4090 - [c14]ChangYong Oh, Kamil Adamczewski, Mijung Park:
Radial and Directional Posteriors for Bayesian Deep Learning. AAAI 2020: 5298-5305 - [c13]James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling:
Variational Bayes in Private Settings (VIPS) (Extended Abstract). IJCAI 2020: 5050-5054 - [i16]Frederik Harder, Kamil Adamczewski, Mijung Park:
Differentially Private Mean Embeddings with Random Features (DP-MERF) for Simple & Practical Synthetic Data Generation. CoRR abs/2002.11603 (2020) - [i15]Kamil Adamczewski, Frederik Harder, Mijung Park:
Q-FIT: The Quantifiable Feature Importance Technique for Explainable Machine Learning. CoRR abs/2010.13872 (2020) - [i14]Kamil Adamczewski, Mijung Park:
Dirichlet Pruning for Neural Network Compression. CoRR abs/2011.05985 (2020)
2010 – 2019
- 2019
- [c12]Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park:
A Differentially Private Kernel Two-Sample Test. ECML/PKDD (1) 2019: 697-724 - [i13]ChangYong Oh, Kamil Adamczewski, Mijung Park:
Radial and Directional Posteriors for Bayesian Neural Networks. CoRR abs/1902.02603 (2019) - [i12]Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet:
Private Causal Inference using Propensity Scores. CoRR abs/1905.12592 (2019) - [i11]Frederik Harder, Matthias Bauer, Mijung Park:
Interpretable and Differentially Private Predictions. CoRR abs/1906.02004 (2019) - [i10]Kamil Adamczewski, Mijung Park:
Neuron ranking - an informed way to condense convolutional neural networks architecture. CoRR abs/1907.02519 (2019) - [i9]Mijung Park, Wittawat Jitkrittum:
ABCDP: Approximate Bayesian Computation Meets Differential Privacy. CoRR abs/1910.05103 (2019) - [i8]Frederik Harder, Jonas Köhler, Max Welling, Mijung Park:
DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning. CoRR abs/1910.06924 (2019) - 2018
- [j4]Adam S. Charles, Mijung Park, J. Patrick Weller, Gregory D. Horwitz, Jonathan W. Pillow:
Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability. Neural Comput. 30(4) (2018) - [i7]Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park:
A Differentially Private Kernel Two-Sample Test. CoRR abs/1808.00380 (2018) - 2017
- [c11]Mijung Park, James R. Foulds, Kamalika Choudhary, Max Welling:
DP-EM: Differentially Private Expectation Maximization. AISTATS 2017: 896-904 - 2016
- [c10]Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic:
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. AISTATS 2016: 398-407 - [i6]Mijung Park, Jimmy Foulds, Kamalika Chaudhuri, Max Welling:
Practical Privacy For Expectation Maximization. CoRR abs/1605.06995 (2016) - [i5]Mijung Park, Max Welling:
A note on privacy preserving iteratively reweighted least squares. CoRR abs/1605.07511 (2016) - [i4]Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling:
Private Topic Modeling. CoRR abs/1609.04120 (2016) - [i3]Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling:
Variational Bayes In Private Settings (VIPS). CoRR abs/1611.00340 (2016) - 2015
- [c9]Mijung Park, Gergo Bohner, Jakob H. Macke:
Unlocking neural population non-stationarities using hierarchical dynamics models. NIPS 2015: 145-153 - [c8]Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltán Szabó, Lars Buesing, Maneesh Sahani:
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM). NIPS 2015: 154-162 - [i2]Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic:
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. CoRR abs/1502.02558 (2015) - 2014
- [j3]Mijung Park, J. Patrick Weller, Gregory D. Horwitz, Jonathan W. Pillow:
Bayesian Active Learning of Neural Firing Rate Maps with Transformed Gaussian Process Priors. Neural Comput. 26(8): 1519-1541 (2014) - [c7]Anqi Wu, Mijung Park, Oluwasanmi Koyejo, Jonathan W. Pillow:
Sparse Bayesian structure learning with dependent relevance determination priors. NIPS 2014: 1628-1636 - 2013
- [j2]Mijung Park, Marcel Nassar, Haris Vikalo:
Bayesian Active Learning for Drug Combinations. IEEE Trans. Biomed. Eng. 60(11): 3248-3255 (2013) - [c6]Mijung Park, Oluwasanmi Koyejo, Joydeep Ghosh, Russell A. Poldrack, Jonathan W. Pillow:
Bayesian Structure Learning for Functional Neuroimaging. AISTATS 2013: 489-497 - [c5]Mijung Park, Jonathan W. Pillow:
Bayesian inference for low rank spatiotemporal neural receptive fields. NIPS 2013: 2688-2696 - 2012
- [c4]Mijung Park, Jonathan W. Pillow:
Bayesian active learning with localized priors for fast receptive field characterization. NIPS 2012: 2357-2365 - [c3]Mijung Park, Marcel Nassar, Brian L. Evans, Haris Vikalo:
Adaptive experimental design for drug combinations. SSP 2012: 712-715 - [i1]Il Memming Park, Marcel Nassar, Mijung Park:
Active Bayesian Optimization: Minimizing Minimizer Entropy. CoRR abs/1202.2143 (2012) - 2011
- [j1]Mijung Park, Jonathan W. Pillow:
Receptive Field Inference with Localized Priors. PLoS Comput. Biol. 7(10) (2011) - [c2]Zrinka Puljiz, Mijung Park, Robert W. Heath Jr.:
A Machine Learning Approach to Link Adaptation for SC-FDE System. GLOBECOM 2011: 1-5 - [c1]Mijung Park, Greg Horwitz, Jonathan W. Pillow:
Active learning of neural response functions with Gaussian processes. NIPS 2011: 2043-2051
Coauthor Index
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