not-MIWAE: Deep Generative Modelling with Missing not at Random Data

• URL: http://arxiv.org/abs/2006.12871v2
• Date: Thu, 18 Mar 2021 13:32:09 GMT
• Title: not-MIWAE: Deep Generative Modelling with Missing not at Random Data
• Authors: Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
• Abstract summary: We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data. We show on various kinds of data sets and missingness patterns that explicitly modelling the missing process can be invaluable.
• Score: 21.977065542645082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data. This allows for incorporating prior information about the type of missingness (e.g. self-censoring) into the model. Our inference technique, based on importance-weighted variational inference, involves maximising a lower bound of the joint likelihood. Stochastic gradients of the bound are obtained by using the reparameterisation trick both in latent space and data space. We show on various kinds of data sets and missingness patterns that explicitly modelling the missing process can be invaluable.

Related papers

• MissDiff: Training Diffusion Models on Tabular Data with Missing Values [29.894691645801597]
  This work presents a unified and principled diffusion-based framework for learning from data with missing values. We first observe that the widely adopted "impute-then-generate" pipeline may lead to a biased learning objective. We prove the proposed method is consistent in learning the score of data distributions, and the proposed training objective serves as an upper bound for the negative likelihood in certain cases.
  arXiv  Detail & Related papers  (2023-07-02T03:49:47Z)
• ChiroDiff: Modelling chirographic data with Diffusion Models [132.5223191478268]
  We introduce a powerful model-class namely "Denoising Diffusion Probabilistic Models" or DDPMs for chirographic data. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate.
  arXiv  Detail & Related papers  (2023-04-07T15:17:48Z)
• Posterior Collapse and Latent Variable Non-identifiability [54.842098835445]
  We propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.
  arXiv  Detail & Related papers  (2023-01-02T06:16:56Z)
• Learning from aggregated data with a maximum entropy model [73.63512438583375]
  We show how a new model, similar to a logistic regression, may be learned from aggregated data only by approximating the unobserved feature distribution with a maximum entropy hypothesis. We present empirical evidence on several public datasets that the model learned this way can achieve performances comparable to those of a logistic model trained with the full unaggregated data.
  arXiv  Detail & Related papers  (2022-10-05T09:17:27Z)
• Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models [0.0]
  Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a sequential selection method that identifies critically important information within a dataset. We find these instabilities are a result of the complexity of the underlying map and linked to extreme events and heavy tails.
  arXiv  Detail & Related papers  (2022-08-27T19:43:53Z)
• Sharing pattern submodels for prediction with missing values [12.981974894538668]
  Missing values are unavoidable in many applications of machine learning and present challenges both during training and at test time. We propose an alternative approach, called sharing pattern submodels, which i) makes predictions robust to missing values at test time, ii) maintains or improves the predictive power of pattern submodels andiii) has a short description, enabling improved interpretability.
  arXiv  Detail & Related papers  (2022-06-22T15:09:40Z)
• Model-based Clustering with Missing Not At Random Data [0.8777702580252754]
  We propose model-based clustering algorithms designed to handle very general types of missing data, including MNAR data. Several MNAR models are discussed, for which the cause of the missingness can depend on both the values of the missing variable themselves and on the class membership. We focus on a specific MNAR model, called MNARz, for which the missingness only depends on the class membership.
  arXiv  Detail & Related papers  (2021-12-20T09:52:12Z)
• Probabilistic Modeling for Human Mesh Recovery [73.11532990173441]
  This paper focuses on the problem of 3D human reconstruction from 2D evidence. We recast the problem as learning a mapping from the input to a distribution of plausible 3D poses.
  arXiv  Detail & Related papers  (2021-08-26T17:55:11Z)
• Contrastive Model Inversion for Data-Free Knowledge Distillation [60.08025054715192]
  We propose Contrastive Model Inversion, where the data diversity is explicitly modeled as an optimizable objective. Our main observation is that, under the constraint of the same amount of data, higher data diversity usually indicates stronger instance discrimination. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CMI achieves significantly superior performance when the generated data are used for knowledge distillation.
  arXiv  Detail & Related papers  (2021-05-18T15:13:00Z)
• Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness [0.0]
  We propose a variational autoencoder architecture to model both ignorable and nonignorable missing data. Our model explicitly learns to cluster the missing data into missingness pattern sets based on the observed data and missingness masks. Our setup trades off the characteristics of ignorable and nonignorable missingness and can thus be applied to data of both types.
  arXiv  Detail & Related papers  (2021-03-05T08:21:35Z)
• Variational Bayesian Unlearning [54.26984662139516]
  We study the problem of approximately unlearning a Bayesian model from a small subset of the training data to be erased. We show that it is equivalent to minimizing an evidence upper bound which trades off between fully unlearning from erased data vs. not entirely forgetting the posterior belief. In model training with VI, only an approximate (instead of exact) posterior belief given the full data can be obtained, which makes unlearning even more challenging.
  arXiv  Detail & Related papers  (2020-10-24T11:53:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.