Margarita Vinaroz, Mohammad Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mijung Park, Hermite Polynomial Features for Private Data Generation , ICML 2022
Fredrik Harder, Milad Jalali Asadabadi, Danica J. Sutherland, Mijung Park, Differentially Private Data Generation Needs Better Features, Under review
Mijung Park, Margarita Vinaroz, Wittawat Jitkrittum, ABCDP: Approximate Bayesian Computation and Differential Privacy , Special Issue on Approximate Bayesian Inference, Entropy 2021
Frederik Harder, Kamil Adamczewski, Mijung Park, DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generation , AISTATS 2021
Kamil Adamczewski, Mijung Park, Dirichlet Pruning for Neural Network Compression
, AISTATS 2021
Mijung Park, James Foulds, Kamalika Chaudhuri, Max Welling, Variational Bayes in private settings (VIPS), JAIR 2020
Frederik Harder, Matthias Bauer, Mijung Park, Interpretable and Differentially Private Predictions , AAAI 2020
Changyong Oh, Kamil Adamczewski, Mijung Park, Radial and Directional Posteriors for Bayesian Neural Networks , AAAI 2020
Kamil Adamczewski, Frederik Harder, Mijung Park, Q-FIT: The Quantifiable Feature Importance Technique forExplainable Machine Learning
, Under review
Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park, A Differentially Private Kernel Two-Sample Test, ECML (European Conference on Machine Learning) 2019
Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet, Private Causal Inference using Propensity Scores , Under review, Arxiv
Frederik Harder, Jonas Koehler, Max Welling, Mijung Park, Differentially private methods of auxiliary coordinates for deep learning (DP-MAC) , NeurIPS workshop 2018, selected for an oral presentation
Adam S. Charles, Mijung Park, J. Patrick Weller, Gregory D. Horwitz and Jonathan W. Pillow, Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability, Neural Computation 2018
Mijung Park, James Foulds, Kamalika Chaudhuri, Max Welling, Practical privacy for expectation maximization, AISTATS 2017
Mijung Park, James Foulds, Kamalika Chaudhuri, Max Welling, Private topic modeling, NIPS workshops 2016, selected for an oral presentation
Mijung Park, Max Welling, A note on privacy preserving iteratively reweighted least squares , ICML workshops 2016
Mijung Park*, Wittawat Jitkrittum*, Dino Sejdinovic, K2-ABC: Approximate Bayesian computation via kernel embeddings , *equally contributed, AISTATS 2016, selected for an oral presentation, top 1\% of accepted papers
Jonathan Pillow, Mijung Park, Adaptive Bayesian methods for closed-loop neurophysiology, Elsevier, ISBN 9780128024522, 2016
Mijung Park, Wittawat, Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing, Maneesh Sahani, Bayesian manifold learning: locally linear latent variable model (LL-LVM) , NIPS 2015
Mijung Park, Gergo Bohner, Jakob Macke, Unlocking neural population non-stationarity using a hierarchical dynamics model, NIPS 2015
Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic, K2-ABC: Approximate Bayesian Computation with Infinite Dimensional Summary Statistics via Kernel Embeddings , arXiv:1502.02558, 2015
Anqi Wu, Mijung Park, Oluwasanmi Koyejo, Jonathan Pillow , Sparse Bayesian structure learning with dependent relevance determination priors , NIPS 2014
Mijung Park, Marcel Nassar, Variational Bayesian inference for forecasting hierarchical time series,
ICML workshop, 2014
Mijung Park, J. Patrick Weller, Greg Horwitz, Jonathan Pillow, Bayesian active learning of neural firing rate maps with
transformed Gaussian process priors, Neural Computation, 2014
Mijung Park, Jonathan Pillow, Bayesian neural receptive field inference with low-rank priors, CoSyNe 2014
Mijung Park, Marcel Nassar, Haris Vikalo, Bayesian active learning for drug
combinations, IEEE transactions on Biomedical engineering, 2013
Mijung Park, Jonathan Pillow, Bayesian neural receptive field inference with low-rank priors , NIPS 2013
Mijung Park*, Oluwasanmi Koyejo*, Joydeep Ghosh, Russell Poldrack, Jonathan Pillow, Bayesian structure learning for functional neuroimaging,
AISTATS 2013, *equally contributed
Mijung Park, J. Patrick Weller, Greg Horwitz, Jonathan Pillow, Adaptive estimation of firing rate maps under super-Poisson variability, CoSyNe 2013
Mijung Park, Jonathan Pillow, Bayesian active learning with localized priors
for fast receptive field characterization, NIPS 2012
Mijung Park, Marcel Nassar, Brian Evans, Haris Vikalo, Adaptive experimental design for drug combinations , IEEE Statistical Signal Processing 2012.
Il Memming Park, Marcel Nassar, Mijung Park, Active Bayesian optimization: minimizing minimizer entropy , arXiv:1202.2143, 2012
Mijung Park, Greg Horwitz, Jonathan Pillow, Adaptive estimation of nonlinear response functions in V1 with Gaussian processes, CoSyNe 2012
Mijung Park, Jonathan Pillow, Receptive field inference with localized priors, PLoS Computational Biology 7 (10), 2011 * selected by F1000 and placed in the top 2 % of published articles in biology and medicine
Mijung Park, Greg Horwitz, Jonathan Pillow, Active learning of neural response functions with Gaussian processes, NIPS 2011, selected for a spotlight presentation.
Zrinka Puljiz, Mijung Park, Robert Heath Jr., A machine learning approach to link adaptation for SC-FDE system, IEEE GlobeCom 2011
Mijung Park, Jonathan Pillow, Empirical Bayes methods for sparse, smooth, localized receptive field estimation, CoSyNe 2011