On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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5 results for “self-censoring”

Learning High-dimensional Gaussians from Censored Datacs.LG

Efficiently learns Gaussian distributions from censored data with known missingness patterns.

problem Learning high-dimensional Gaussian distributions from data with missing values.

method Developed algorithms for self-censoring and linear thresholding scenarios.

result Achieved efficient learning of Gaussian distributions up to a specified total variation distance.

Gaussian distribution distribution learning censored data missing values self-censoring

2025-04-28abs ↗pdf ↗

On Testability and Goodness of Fit Tests in Missing Data Modelsstat.ME

The paper introduces tests for missing data models based on graph assumptions.

problem Verification of assumptions in missing data models is insufficiently addressed.

method The paper explores three classes of missing data models and designs goodness-of-fit tests.

result The paper provides new insights and tests for missing data graphical models.

missing data graphical models goodness-of-fit tests verification longitudinal studies

2022-02-28abs ↗pdf ↗

not-MIWAE: Deep Generative Modelling with Missing not at Random Datastat.ML

Deep learning for missing data with flexible modelling.

problem Missing data with missingness dependent on the missing values.

method Deep latent variable models with a neural network for missingness pattern modeling, importance-weighted variational inference, and stochastic gradients.

result Explicitly modelling missingness improves inference on various data sets.

deep learning missing data missingness modeling variational inference neural networks

2020-06-23abs ↗pdf ↗

Identifiable Deep Latent Variable Models for MNAR Datastat.ME

Proposes a deep latent variable model for MNAR data.

problem Missing data leading to biased results in MAR assumptions.

method Deep latent variable models with conditional no self-censoring.

result Establishes identifiability of MNAR data distribution.

missing data missing not at random deep learning latent variable models identifiability

2026-03-25abs ↗pdf ↗

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacycs.CR

This paper presents a method to generate synthetic data with differential privacy to protect user privacy while maintaining data trends.

problem Protecting user privacy while using real data for AI applications.

method Develops a practical guide to generating synthetic data using differential privacy.

result Synthetic data can be generated to preserve trends of real data while ensuring strong privacy protections.

Differential Privacy Synthetic Data AI Privacy Data Privacy Privacy Protection

2025-12-02abs ↗pdf ↗