
Polynomial magic! Hermite polynomials for private data generation
Kernel mean embedding is a useful tool to compare probability measures. ...
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Dirichlet Pruning for Neural Network Compression
We introduce Dirichlet pruning, a novel postprocessing technique to tra...
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QFIT: 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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Differentially Private Mean Embeddings with Random Features (DPMERF) for Simple Practical Synthetic Data Generation
We present a differentially private data generation paradigm using rando...
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DPMAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning
Developing a differentially private deep learning algorithm is challengi...
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ABCDP: Approximate Bayesian Computation Meets Differential Privacy
We develop a novel approximate Bayesian computation (ABC) framework, ABC...
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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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Interpretable and Differentially Private Predictions
Interpretable predictions, where it is clear why a machine learning mode...
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Private Causal Inference using Propensity Scores
The use of propensity score methods to reduce selection bias when determ...
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Radial and Directional Posteriors for Bayesian Neural Networks
We propose a new variational family for Bayesian neural networks. We dec...
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A Differentially Private Kernel TwoSample Test
Kernel twosample testing is a useful statistical tool in determining wh...
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Variational Bayes In Private Settings (VIPS)
We provide a general framework for privacypreserving variational Bayes ...
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Private Topic Modeling
We develop a privatised stochastic variational inference method for Late...
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A note on privacy preserving iteratively reweighted least squares
Iteratively reweighted least squares (IRLS) is a widelyused method in m...
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DPEM: Differentially Private Expectation Maximization
The iterative nature of the expectation maximization (EM) algorithm pres...
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K2ABC: Approximate Bayesian Computation with Kernel Embeddings
Complicated generative models often result in a situation where computin...
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Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LLLVM)
We introduce the Locally Linear Latent Variable Model (LLLVM), a probab...
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Hierarchical models for neural population dynamics in the presence of nonstationarity
Neural population activity often exhibits rich variability and temporal ...
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Active Bayesian Optimization: Minimizing Minimizer Entropy
The ultimate goal of optimization is to find the minimizer of a target f...
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Mijung Park
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