
Learning Generative Models using Denoising Density Estimators
Learning generative probabilistic models that can estimate the continuou...
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Roundtrip: A Deep Generative Neural Density Estimator
Density estimation is a fundamental problem in both statistics and machi...
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Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale
Increasing interest in privacypreserving machine learning has led to ne...
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DDSketch: A fast and fullymergeable quantile sketch with relativeerror guarantees
Summary statistics such as the mean and variance are easily maintained f...
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A OnePass Private Sketch for Most Machine Learning Tasks
Differential privacy (DP) is a compelling privacy definition that explai...
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Learning Implicit Generative Models with Theoretical Guarantees
We propose a unified framework for implicit generative modeling (UnifiGe...
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Robust Regression for Automatic Fusion Plasma Analysis based on Generative Modeling
The first step to realize automatic experimental data analysis for fusio...
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Density Sketches for Sampling and Estimation
We introduce Density sketches (DS): a succinct online summary of the data distribution. DS can accurately estimate point wise probability density. Interestingly, DS also provides a capability to sample unseen novel data from the underlying data distribution. Thus, analogous to popular generative models, DS allows us to succinctly replace the realdata in almost all machine learning pipelines with synthetic examples drawn from the same distribution as the original data. However, unlike generative models, which do not have any statistical guarantees, DS leads to theoretically sound asymptotically converging consistent estimators of the underlying density function. Density sketches also have many appealing properties making them ideal for largescale distributed applications. DS construction is an online algorithm. The sketches are additive, i.e., the sum of two sketches is the sketch of the combined data. These properties allow data to be collected from distributed sources, compressed into a density sketch, efficiently transmitted in the sketch form to a central server, merged, and resampled into a synthetic database for modeling applications. Thus, density sketches can potentially revolutionize how we store, communicate, and distribute data.
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