A Machine Learning Approach to Link Adaptation for SC-FDE System. - Zrinka Puljiz; Mijung Park; Robert W. Heath Jr. (GLOBECOM, 2011)

Active learning of neural response functions with Gaussian processes. - Mijung Park; Greg Horwitz; Jonathan W. Pillow (NIPS, 2011)

Receptive Field Inference with Localized Priors. - Mijung Park; Jonathan W. Pillow (PLoS Comput. Biol., 2011)

Bayesian active learning with localized priors for fast receptive field characterization. - Mijung Park; Jonathan W. Pillow (NIPS, 2012)

Adaptive experimental design for drug combinations. - Mijung Park; Marcel Nassar; Brian L. Evans; Haris Vikalo (SSP, 2012)

Active Bayesian Optimization: Minimizing Minimizer Entropy - Il Memming Park; Marcel Nassar; Mijung Park (CoRR, 2012)

Bayesian Structure Learning for Functional Neuroimaging. - Mijung Park; Oluwasanmi Koyejo; Joydeep Ghosh; Russell A. Poldrack; Jonathan W. Pillow (AISTATS, 2013)

Bayesian inference for low rank spatiotemporal neural receptive fields. - Mijung Park; Jonathan W. Pillow (NIPS, 2013)

Bayesian Active Learning for Drug Combinations. - Mijung Park; Marcel Nassar; Haris Vikalo (IEEE Trans. Biomed. Eng., 2013)

Sparse Bayesian structure learning with dependent relevance determination priors. - Anqi Wu; Mijung Park; Oluwasanmi Koyejo; Jonathan W. Pillow (NIPS, 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)

Unlocking neural population non-stationarities using hierarchical dynamics models. - Mijung Park; Gergo Bohner; Jakob H. Macke (NIPS, 2015)

Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM). - Mijung Park; Wittawat Jitkrittum; Ahmad Qamar; Zoltán Szabó 001; Lars Buesing; Maneesh Sahani (NIPS, 2015)

K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. - Mijung Park; Wittawat Jitkrittum; Dino Sejdinovic (AISTATS, 2016)

Practical Privacy For Expectation Maximization. - Mijung Park; Jimmy Foulds; Kamalika Chaudhuri; Max Welling (CoRR, 2016)

Private Topic Modeling. - Mijung Park; James R. Foulds; Kamalika Chaudhuri; Max Welling (CoRR, 2016)

A note on privacy preserving iteratively reweighted least squares. - Mijung Park; Max Welling (CoRR, 2016)

DP-EM: Differentially Private Expectation Maximization. - Mijung Park; James R. Foulds; Kamalika Choudhary; Max Welling (AISTATS, 2017)

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)

A Differentially Private Kernel Two-Sample Test. - Anant Raj; Ho Chung Leon Law; Dino Sejdinovic; Mijung Park (ECML/PKDD (1), 2019)

Radial and Directional Posteriors for Bayesian Neural Networks. - ChangYong Oh; Kamil Adamczewski; Mijung Park (CoRR, 2019)

Private Causal Inference using Propensity Scores. - Si Kai Lee; Luigi Gresele; Mijung Park; Krikamol Muandet (CoRR, 2019)

Neuron ranking - an informed way to condense convolutional neural networks architecture. - Kamil Adamczewski; Mijung Park (CoRR, 2019)

ABCDP: Approximate Bayesian Computation Meets Differential Privacy. - Mijung Park; Wittawat Jitkrittum (CoRR, 2019)

DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning. - Frederik Harder; Jonas Köhler 001; Max Welling; Mijung Park (CoRR, 2019)

Interpretable and Differentially Private Predictions. - Frederik Harder; Matthias Bauer; Mijung Park (AAAI, 2020)

Radial and Directional Posteriors for Bayesian Deep Learning. - ChangYong Oh; Kamil Adamczewski; Mijung Park (AAAI, 2020)

Variational Bayes in Private Settings (VIPS) (Extended Abstract). - James R. Foulds; Mijung Park; Kamalika Chaudhuri; Max Welling (IJCAI, 2020)

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

Dirichlet Pruning for Neural Network Compression. - Kamil Adamczewski; Mijung Park (CoRR, 2020)

Variational Bayes In Private Settings (VIPS). - Mijung Park; James R. Foulds; Kamalika Chaudhuri; Max Welling (J. Artif. Intell. Res., 2020)

Dirichlet Pruning for Convolutional Neural Networks. - Kamil Adamczewski; Mijung Park (AISTATS, 2021)

DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation. - Frederik Harder; Kamil Adamczewski; Mijung Park (AISTATS, 2021)

Polynomial magic! Hermite polynomials for private data generation. - Mijung Park; Margarita Vinaroz; Mohammad-Amin Charusaie; Frederik Harder (CoRR, 2021)

DP-SEP! Differentially Private Stochastic Expectation Propagation. - Margarita Vinaroz; Mijung Park (CoRR, 2021)

ABCDP: Approximate Bayesian Computation with Differential Privacy. - Mijung Park; Margarita Vinaroz; Wittawat Jitkrittum (Entropy, 2021)

Hermite Polynomial Features for Private Data Generation. - Margarita Vinaroz; Mohammad-Amin Charusaie; Frederik Harder; Kamil Adamczewski; Mijung Park (ICML, 2022)

Differentially Private Data Generation Needs Better Features. - Frederik Harder; Milad Jalali Asadabadi; Danica J. Sutherland; Mijung Park (CoRR, 2022)

Differentially Private Stochastic Expectation Propagation. - Margarita Vinaroz; Mijung Park (Trans. Mach. Learn. Res., 2022)

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

Differential Privacy Meets Neural Network Pruning. - Kamil Adamczewski; Mijung Park (CoRR, 2023)

Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification. - Kamil Adamczewski; Yingchen He; Mijung Park (CoRR, 2023)

Pre-trained Perceptual Features Improve Differentially Private Image Generation. - Frederik Harder; Milad Jalali; Danica J. Sutherland; Mijung Park (Trans. Mach. Learn. Res., 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., 2023)

Differentially Private Neural Tangent Kernels (DP-NTK) for Privacy-Preserving Data Generation. - Yi Yang 001; Kamil Adamczewski; Xiaoxiao Li; Danica J. Sutherland; Mijung Park (J. Artif. Intell. Res., 2024)

Differentially Private Latent Diffusion Models. - Michael F. Liu; Saiyue Lyu; Margarita Vinaroz; Mijung Park (Trans. Mach. Learn. Res., 2024)

Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation. - Margarita Vinaroz; Mijung Park (Trans. Mach. Learn. Res., 2024)

Bayesian Principles Improve Prompt Learning In Vision-Language Models. - Mingyu Kim; Jongwoo Ko; Mijung Park (AISTATS, 2025)

Training-Free Safe Denoisers for Safe Use of Diffusion Models. - Mingyu Kim; Dongjun Kim; Amman Yusuf; Stefano Ermon; Mijung Park (CoRR, 2025)

Revisiting Bayesian Model Averaging in the Era of Foundation Models. - Mijung Park (CoRR, 2025)