Park, Mijung

27 publications

AISTATS 2025 Bayesian Principles Improve Prompt Learning in Vision-Language Models Mingyu Kim, Jongwoo Ko, Mijung Park

NeurIPS 2025 Training-Free Safe Denoisers for Safe Use of Diffusion Models Mingyu Kim, Dongjun Kim, Amman Yusuf, Stefano Ermon, Mijung Park

ICLRW 2025 Training-Free Safe Denoisers for Safe Use of Diffusion Models Mingyu Kim, Dongjun Kim, Amman Yusuf, Stefano Ermon, Mijung Park

TMLR 2024 Differentially Private Kernel Inducing Points Using Features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation Margarita Vinaroz, Mijung Park

TMLR 2024 Differentially Private Latent Diffusion Models Michael F Liu, Saiyue Lyu, Margarita Vinaroz, Mijung Park

ICLRW 2024 Differentially Private Latent Diffusion Models Saiyue Lyu, Michael F Liu, Margarita Vinaroz, Mijung Park

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

TMLR 2023 Pre-Trained Perceptual Features Improve Differentially Private Image Generation Frederik Harder, Milad Jalali, Danica J. Sutherland, Mijung Park

TMLR 2022 Differentially Private Stochastic Expectation Propagation Margarita Vinaroz, 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 Dirichlet Pruning for Convolutional Neural Networks Kamil Adamczewski, Mijung Park

NeurIPSW 2021 DP-SEP: Differentially Private Stochastic Expectation Propagation Margarita Vinaroz, Mijung Park, Mijung Park

NeurIPSW 2021 DP-SEP: Differentially Private Stochastic Expectation Propagation Margarita Vinaroz, Mijung Park, Mijung Park

AAAI 2020 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

JAIR 2020 Variational Bayes in Private Settings (VIPS) Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling

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

ECML-PKDD 2019 A Differentially Private Kernel Two-Sample Test Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park

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

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

NeurIPS 2015 Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing, Maneesh Sahani

NeurIPS 2015 Unlocking Neural Population Non-Stationarities Using Hierarchical Dynamics Models Mijung Park, Gergo Bohner, Jakob H. Macke

NeurIPS 2014 Sparse Bayesian Structure Learning with “dependent Relevance Determination” Priors Anqi Wu, Mijung Park, Oluwasanmi O Koyejo, Jonathan W Pillow

NeurIPS 2013 Bayesian Inference for Low Rank Spatiotemporal Neural Receptive Fields Mijung Park, Jonathan W Pillow

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

NeurIPS 2012 Bayesian Active Learning with Localized Priors for Fast Receptive Field Characterization Mijung Park, Jonathan W. Pillow

NeurIPS 2011 Active Learning of Neural Response Functions with Gaussian Processes Mijung Park, Greg Horwitz, Jonathan W. Pillow