Mijung Park

According to our database, Mijung Park authored at least 47 papers between 2011 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Rapid response nursing triage outcomes for COVID-19: factors associated with patient's participation in triage recommendations.

Jyu-Lin Chen, Chen-Xi Lin, Mijung Park, Jerry John Nutor, Rosalind de Lisser, Thomas J. Hoffmann, Hannah J. Kim

BMC Medical Informatics Decis. Mak., December, 2023

Pre-trained Perceptual Features Improve Differentially Private Image Generation.

Frederik Harder, Milad Jalali, Danica J. Sutherland, Mijung Park

Trans. Mach. Learn. Res., 2023

Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification.

Kamil Adamczewski, Yingchen He, Mijung Park

CoRR, 2023

Differentially Private Latent Diffusion Models.

Saiyue Lyu, Margarita Vinaroz, Michael F. Liu, Mijung Park

CoRR, 2023

Differential Privacy Meets Neural Network Pruning.

Kamil Adamczewski, Mijung Park

CoRR, 2023

Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation.

Yilin Yang, Kamil Adamczewski, Danica J. Sutherland, Xiaoxiao Li, Mijung Park

CoRR, 2023

Differentially Private Kernel Inducing Points (DP-KIP) for Privacy-preserving Data Distillation.

Margarita Vinaroz, Mijung Park

CoRR, 2023

2022

Differentially Private Stochastic Expectation Propagation.

Margarita Vinaroz, Mijung Park

Trans. Mach. Learn. Res., 2022

Differentially Private Data Generation Needs Better Features.

Frederik Harder, Milad Jalali Asadabadi, Danica J. Sutherland, Mijung Park

CoRR, 2022

Hermite Polynomial Features for Private Data Generation.

Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mijung Park

Proceedings of the International Conference on Machine Learning, 2022

2021

ABCDP: Approximate Bayesian Computation with Differential Privacy.

Mijung Park, Margarita Vinaroz, Wittawat Jitkrittum

Entropy, 2021

DP-SEP! Differentially Private Stochastic Expectation Propagation.

Margarita Vinaroz, Mijung Park

CoRR, 2021

Polynomial magic! Hermite polynomials for private data generation.

Mijung Park, Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder

CoRR, 2021

DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation.

Frederik Harder, Kamil Adamczewski, Mijung Park

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Dirichlet Pruning for Convolutional Neural Networks.

Kamil Adamczewski, Mijung Park

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Variational Bayes In Private Settings (VIPS).

Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling

J. Artif. Intell. Res., 2020

Dirichlet Pruning for Neural Network Compression.

Kamil Adamczewski, Mijung Park

CoRR, 2020

Q-FIT: The Quantifiable Feature Importance Technique for Explainable Machine Learning.

Kamil Adamczewski, Frederik Harder, Mijung Park

CoRR, 2020

Differentially Private Mean Embeddings with Random Features (DP-MERF) for Simple & Practical Synthetic Data Generation.

Frederik Harder, Kamil Adamczewski, Mijung Park

CoRR, 2020

Variational Bayes in Private Settings (VIPS) (Extended Abstract).

James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Radial and Directional Posteriors for Bayesian Deep Learning.

ChangYong Oh, Kamil Adamczewski, Mijung Park

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Interpretable and Differentially Private Predictions.

Frederik Harder, Matthias Bauer, Mijung Park

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning.

Frederik Harder, Jonas Köhler, Max Welling, Mijung Park

CoRR, 2019

ABCDP: Approximate Bayesian Computation Meets Differential Privacy.

Mijung Park, Wittawat Jitkrittum

CoRR, 2019

Neuron ranking - an informed way to condense convolutional neural networks architecture.

Kamil Adamczewski, Mijung Park

CoRR, 2019

Private Causal Inference using Propensity Scores.

Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet

CoRR, 2019

Radial and Directional Posteriors for Bayesian Neural Networks.

ChangYong Oh, Kamil Adamczewski, Mijung Park

CoRR, 2019

A Differentially Private Kernel Two-Sample Test.

Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

2018

Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability.

Adam S. Charles, Mijung Park, J. Patrick Weller, Gregory D. Horwitz, Jonathan W. Pillow

Neural Comput., 2018

2017

DP-EM: Differentially Private Expectation Maximization.

Mijung Park, James R. Foulds, Kamalika Choudhary, Max Welling

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

A note on privacy preserving iteratively reweighted least squares.

Mijung Park, Max Welling

CoRR, 2016

Private Topic Modeling.

Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling

CoRR, 2016

Practical Privacy For Expectation Maximization.

Mijung Park, Jimmy Foulds, Kamalika Chaudhuri, Max Welling

CoRR, 2016

K2-ABC: Approximate Bayesian Computation with Kernel Embeddings.

Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM).

Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltán Szabó, Lars Buesing, Maneesh Sahani

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Unlocking neural population non-stationarities using hierarchical dynamics models.

Mijung Park, Gergo Bohner, Jakob H. Macke

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Bayesian Active Learning of Neural Firing Rate Maps with Transformed Gaussian Process Priors.

Mijung Park, J. Patrick Weller, Gregory D. Horwitz, Jonathan W. Pillow

Neural Comput., 2014

Sparse Bayesian structure learning with dependent relevance determination priors.

Anqi Wu, Mijung Park, Oluwasanmi Koyejo, Jonathan W. Pillow

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Bayesian Active Learning for Drug Combinations.

Mijung Park, Marcel Nassar, Haris Vikalo

IEEE Trans. Biomed. Eng., 2013

Bayesian inference for low rank spatiotemporal neural receptive fields.

Mijung Park, Jonathan W. Pillow

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Bayesian Structure Learning for Functional Neuroimaging.

Mijung Park, Oluwasanmi Koyejo, Joydeep Ghosh, Russell A. Poldrack, Jonathan W. Pillow

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Active Bayesian Optimization: Minimizing Minimizer Entropy

Il Memming Park, Marcel Nassar, Mijung Park

CoRR, 2012

Adaptive experimental design for drug combinations.

Mijung Park, Marcel Nassar, Brian L. Evans, Haris Vikalo

Proceedings of the IEEE Statistical Signal Processing Workshop, 2012

Bayesian active learning with localized priors for fast receptive field characterization.

Mijung Park, Jonathan W. Pillow

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Receptive Field Inference with Localized Priors.

Mijung Park, Jonathan W. Pillow

PLoS Comput. Biol., 2011

Active learning of neural response functions with Gaussian processes.

Mijung Park, Greg Horwitz, Jonathan W. Pillow

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

A Machine Learning Approach to Link Adaptation for SC-FDE System.

Zrinka Puljiz, Mijung Park, Robert W. Heath Jr.

Proceedings of the Global Communications Conference, 2011