• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 51 matches

• 2023
• 
  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)
• 
  Frederik Harder, Milad Jalali, Danica J. Sutherland, Mijung Park:
  Pre-trained Perceptual Features Improve Differentially Private Image Generation. Trans. Mach. Learn. Res. 2023 (2023)
• 
  Margarita Vinaroz, Mijung Park:
  Differentially Private Kernel Inducing Points (DP-KIP) for Privacy-preserving Data Distillation. CoRR abs/2301.13389 (2023)
• 
  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)
• 
  Kamil Adamczewski, Mijung Park:
  Differential Privacy Meets Neural Network Pruning. CoRR abs/2303.04612 (2023)
• 
  Saiyue Lyu, Margarita Vinaroz, Michael F. Liu, Mijung Park:
  Differentially Private Latent Diffusion Models. CoRR abs/2305.15759 (2023)
• 
  Kamil Adamczewski, Yingchen He, Mijung Park:
  Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification. CoRR abs/2306.11754 (2023)
• 2022
• 
  Margarita Vinaroz, Mijung Park:
  Differentially Private Stochastic Expectation Propagation. Trans. Mach. Learn. Res. 2022 (2022)
• 
  Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mijung Park:
  Hermite Polynomial Features for Private Data Generation. ICML 2022: 22300-22324
• 
  Frederik Harder, Milad Jalali Asadabadi, Danica J. Sutherland, Mijung Park:
  Differentially Private Data Generation Needs Better Features. CoRR abs/2205.12900 (2022)
• 2021
• 
  Mijung Park, Margarita Vinaroz, Wittawat Jitkrittum:
  ABCDP: Approximate Bayesian Computation with Differential Privacy. Entropy 23(8): 961 (2021)
• 
  Kamil Adamczewski, Mijung Park:
  Dirichlet Pruning for Convolutional Neural Networks. AISTATS 2021: 3637-3645
• 
  Frederik Harder, Kamil Adamczewski, Mijung Park:
  DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation. AISTATS 2021: 1819-1827
• 
  Mijung Park, Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder:
  Polynomial magic! Hermite polynomials for private data generation. CoRR abs/2106.05042 (2021)
• 
  Margarita Vinaroz, Mijung Park:
  DP-SEP! Differentially Private Stochastic Expectation Propagation. CoRR abs/2111.13219 (2021)
• 2020
• 
  Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling:
  Variational Bayes In Private Settings (VIPS). J. Artif. Intell. Res. 68: 109-157 (2020)
• 
  Frederik Harder, Matthias Bauer, Mijung Park:
  Interpretable and Differentially Private Predictions. AAAI 2020: 4083-4090
• 
  ChangYong Oh, Kamil Adamczewski, Mijung Park:
  Radial and Directional Posteriors for Bayesian Deep Learning. AAAI 2020: 5298-5305
• 
  James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling:
  Variational Bayes in Private Settings (VIPS) (Extended Abstract). IJCAI 2020: 5050-5054
• 
  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)
• 
  Kamil Adamczewski, Frederik Harder, Mijung Park:
  Q-FIT: The Quantifiable Feature Importance Technique for Explainable Machine Learning. CoRR abs/2010.13872 (2020)
• 
  Kamil Adamczewski, Mijung Park:
  Dirichlet Pruning for Neural Network Compression. CoRR abs/2011.05985 (2020)
• 2019
• 
  Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park:
  A Differentially Private Kernel Two-Sample Test. ECML/PKDD (1) 2019: 697-724
• 
  ChangYong Oh, Kamil Adamczewski, Mijung Park:
  Radial and Directional Posteriors for Bayesian Neural Networks. CoRR abs/1902.02603 (2019)
• 
  Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet:
  Private Causal Inference using Propensity Scores. CoRR abs/1905.12592 (2019)
• 
  Frederik Harder, Matthias Bauer, Mijung Park:
  Interpretable and Differentially Private Predictions. CoRR abs/1906.02004 (2019)
• 
  Kamil Adamczewski, Mijung Park:
  Neuron ranking - an informed way to condense convolutional neural networks architecture. CoRR abs/1907.02519 (2019)
• 
  Mijung Park, Wittawat Jitkrittum:
  ABCDP: Approximate Bayesian Computation Meets Differential Privacy. CoRR abs/1910.05103 (2019)
• 
  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
• 
  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)

skipping 21 more matches