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06/19/2023
Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification
Scalability is a significant challenge when it comes to applying differe...
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05/25/2023
Differentially Private Latent Diffusion Models
Diffusion models (DMs) are widely used for generating high-quality image...
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03/08/2023
Differential Privacy Meets Neural Network Pruning
A major challenge in applying differential privacy to training deep neur...
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03/03/2023
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation
Maximum mean discrepancy (MMD) is a particularly useful distance metric ...
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05/25/2022
Differentially Private Data Generation Needs Better Features
Training even moderately-sized generative models with differentially-pri...
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11/25/2021
DP-SEP! Differentially Private Stochastic Expectation Propagation
We are interested in privatizing an approximate posterior inference algo...
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06/09/2021
Polynomial magic! Hermite polynomials for private data generation
Kernel mean embedding is a useful tool to compare probability measures. ...
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11/10/2020
Dirichlet Pruning for Neural Network Compression
We introduce Dirichlet pruning, a novel post-processing technique to tra...
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10/26/2020
Q-FIT: The Quantifiable Feature Importance Technique for Explainable Machine Learning
We introduce a novel framework to quantify the importance of each input ...
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02/26/2020
Differentially Private Mean Embeddings with Random Features (DP-MERF) for Simple Practical Synthetic Data Generation
We present a differentially private data generation paradigm using rando...
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10/15/2019
DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning
Developing a differentially private deep learning algorithm is challengi...
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10/11/2019
ABCDP: Approximate Bayesian Computation Meets Differential Privacy
We develop a novel approximate Bayesian computation (ABC) framework, ABC...
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07/03/2019
Neuron ranking -- an informed way to condense convolutional neural networks architecture
Convolutional neural networks (CNNs) in recent years have made a dramati...
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06/05/2019
Interpretable and Differentially Private Predictions
Interpretable predictions, where it is clear why a machine learning mode...
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05/29/2019
Private Causal Inference using Propensity Scores
The use of propensity score methods to reduce selection bias when determ...
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02/07/2019
Radial and Directional Posteriors for Bayesian Neural Networks
We propose a new variational family for Bayesian neural networks. We dec...
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08/01/2018
A Differentially Private Kernel Two-Sample Test
Kernel two-sample testing is a useful statistical tool in determining wh...
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11/01/2016
Variational Bayes In Private Settings (VIPS)
We provide a general framework for privacy-preserving variational Bayes ...
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09/14/2016
Private Topic Modeling
We develop a privatised stochastic variational inference method for Late...
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05/24/2016
A note on privacy preserving iteratively reweighted least squares
Iteratively reweighted least squares (IRLS) is a widely-used method in m...
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05/23/2016
DP-EM: Differentially Private Expectation Maximization
The iterative nature of the expectation maximization (EM) algorithm pres...
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02/09/2015
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
Complicated generative models often result in a situation where computin...
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10/24/2014
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
We introduce the Locally Linear Latent Variable Model (LL-LVM), a probab...
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10/12/2014
Hierarchical models for neural population dynamics in the presence of non-stationarity
Neural population activity often exhibits rich variability and temporal ...
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02/09/2012
