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MIWAE: Deep Generative Modelling and Imputation of Incomplete Data [article]

Pierre-Alexandre Mattei, Jes Frellsen
<span title="2019-02-04">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
On various continuous and binary data sets, we also show that MIWAE provides accurate single imputations, and is highly competitive with state-of-the-art methods.  ...  We also develop Monte Carlo techniques for single and multiple imputation using a DLVM trained on an incomplete data set.  ...  Deep generative models and incomplete data The main purpose of this work is to present a simple way to fit a deep latent variable model (DLVM, Kingma & Welling, 2014; Rezende et al., 2014) to an incomplete  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.02633v2">arXiv:1812.02633v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rouuxoskcndetectdxpqjyds3u">fatcat:rouuxoskcndetectdxpqjyds3u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191021000836/https://arxiv.org/pdf/1812.02633v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6f/f0/6ff092446041cb11b110fd9141a7fe847523e908.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.02633v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

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

Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
<span title="2021-03-18">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We show on various kinds of data sets and missingness patterns that explicitly modelling the missing process can be invaluable.  ...  Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data.  ...  Again, such a setting can appear when there is strong geometric structure in the data (e.g. with images or proteins).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.12871v2">arXiv:2006.12871v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jsk4zkr45rgk3hz7mxtipf5ubi">fatcat:jsk4zkr45rgk3hz7mxtipf5ubi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200625040250/https://arxiv.org/pdf/2006.12871v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a6/fe/a6fe8c1d5248cc778689cc3e4126abc4260439b6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.12871v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness [article]

Sahra Ghalebikesabi, Rob Cornish, Luke J. Kelly, Chris Holmes
<span title="2021-03-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We evaluate our method on a wide range of data sets with different types of missingness and achieve state-of-the-art imputation performance.  ...  Our model outperforms many common imputation algorithms, especially when the amount of missing data is high and the missingness mechanism is nonignorable.  ...  CH is supported by The Alan Turing Institute, Health Data Research UK, the Medical Research Council UK, the EPSRC through the Bayes4Health programme Grant EP/R018561/1, and AI for Science and Government  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.03532v1">arXiv:2103.03532v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wfgghu6rxbbxfka75pxo36t3om">fatcat:wfgghu6rxbbxfka75pxo36t3om</a> </span>
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Deep Distribution-preserving Incomplete Clustering with Optimal Transport [article]

Mingjie Luo, Siwei Wang, Xinwang Liu, Wenxuan Tu, Yi Zhang, Xifeng Guo, Sihang Zhou, En Zhu
<span title="2021-03-21">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To solve the problem, we propose a novel deep incomplete clustering method, named Deep Distribution-preserving Incomplete Clustering with Optimal Transport (DDIC-OT).  ...  Although various methods have been proposed, the performance of existing approaches drops dramatically when handling incomplete high-dimensional data (which is common in real world applications).  ...  Therefore, the traditional statistical and deep generative methods fail to impute proper values lacking of sufficient information, e.g., knn-filling and GAN-style solutions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.11424v1">arXiv:2103.11424v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zecohbhhrrdxvhcnhfc5vdjqo4">fatcat:zecohbhhrrdxvhcnhfc5vdjqo4</a> </span>
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MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models [article]

Imke Mayer, Julie Josse, Félix Raimundo, Jean-Philippe Vert
<span title="2020-02-25">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Inferring causal effects of a treatment, intervention or policy from observational data is central to many applications.  ...  Numerical experiments demonstrate the effectiveness of the proposed methodology especially for non-linear models compared to competitors.  ...  of data generated under a LRMF model mentioned in Section 4.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.10837v1">arXiv:2002.10837v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6tln4gh7evbrtg6b4cmcyz47ka">fatcat:6tln4gh7evbrtg6b4cmcyz47ka</a> </span>
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Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data [article]

Yu Gong and Hossein Hajimirsadeghi and Jiawei He and Thibaut Durand and Greg Mori
<span title="2021-02-25">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
It results in a unified model for various downstream tasks including data generation and imputation.  ...  VSAE learns the latent dependencies in heterogeneous data by modeling the joint distribution of observed data, unobserved data, and the imputation mask which represents how the data are missing.  ...  Having a model designed to learn from incomplete data not only increases the application spectrum of deep learning algorithms but also benefits downstream tasks such as data imputation, which remains an  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.12679v1">arXiv:2102.12679v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qmwywecuwnbbtm3b3hjldgcsca">fatcat:qmwywecuwnbbtm3b3hjldgcsca</a> </span>
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Gradient Importance Learning for Incomplete Observations [article]

Qitong Gao, Dong Wang, Joshua D. Amason, Siyang Yuan, Chenyang Tao, Ricardo Henao, Majda Hadziahmetovic, Lawrence Carin, Miroslav Pajic
<span title="2022-03-01">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
state-of-the-art imputation methods.  ...  Though recent works have developed methods that can generate estimates (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assumptions that may not align  ...  Machine learning methods for septic shock Pierre-Alexandre Mattei and Jes Frellsen. Miwae: Deep generative modelling and imputation of incomplete data sets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.01983v4">arXiv:2107.01983v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qumjhvlnlvh4bbeztrijfurlqi">fatcat:qumjhvlnlvh4bbeztrijfurlqi</a> </span>
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Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows [article]

Chris Cannella, Mohammadreza Soltani, Vahid Tarokh
<span title="2021-02-26">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Through experimental tests applying normalizing flows to missing data tasks for a variety of data sets, we demonstrate the efficacy of PL-MCMC for conditional sampling from normalizing flows.  ...  As a conditional sampling method, PL-MCMC enables Monte Carlo Expectation Maximization (MC-EM) training of normalizing flows from incomplete data.  ...  ACKNOWLEDGEMENTS This work was supported by the Office of Naval Research Grant No. N00014-18-1-2244.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.06140v4">arXiv:2007.06140v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/li63bukpajhlpe2fqozrkd6eym">fatcat:li63bukpajhlpe2fqozrkd6eym</a> </span>
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Differentiable and Scalable Generative Adversarial Models for Data Imputation [article]

Yangyang Wu and Jun Wang and Xiaoye Miao and Wenjia Wang and Jianwei Yin
<span title="2022-01-10">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications.  ...  SCIS consists of two modules, differentiable imputation modeling (DIM) and sample size estimation (SSE).  ...  In general, the effective and scalable data imputation over large-scale incomplete data is indispensable in many real-life scenarios.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.03202v1">arXiv:2201.03202v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nhzoo7hixjha5opg5qb6kn2sb4">fatcat:nhzoo7hixjha5opg5qb6kn2sb4</a> </span>
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Networked Time Series Prediction with Incomplete Data [article]

Yichen Zhu, Mengtian Zhang, Bo Jiang, Haiming Jin, Jianqiang Huang, Xinbing Wang
<span title="2021-10-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future.  ...  In this paper, we study the problem of NETS prediction with incomplete data.  ...  P-VAE [27] , MIWAE [28] and P-BiGAN [21] extends VAE [17] , IWAE [4] and BiGAN [10] respectively to an encoding-decoding framework that models the distribution of incomplete data together with  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.02271v1">arXiv:2110.02271v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4pu5temxyjavzhrmbetnq3na5u">fatcat:4pu5temxyjavzhrmbetnq3na5u</a> </span>
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MisConv: Convolutional Neural Networks for Missing Data [article]

Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski, Jacek Tabor
<span title="2021-10-29">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
While imputation-based techniques are still one of the most popular solutions, they frequently introduce unreliable information to the data and do not take into account the uncertainty of estimation, which  ...  In this paper, we present MisConv, a general mechanism, for adapting various CNN architectures to process incomplete images.  ...  Acknowledgement The research of M.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.14010v2">arXiv:2110.14010v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5l6kll3qevhyzdi4oklrnkfjmu">fatcat:5l6kll3qevhyzdi4oklrnkfjmu</a> </span>
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Incomplete Data Analysis [chapter]

Bo-Wei Chen, Jia-Ching Wang
<span title="2021-07-07">2021</span> <i title="IntechOpen"> Applications of Pattern Recognition </i> &nbsp;
When a dataset contains missing values, nonvectorial data are generated.  ...  At present, a great deal of effort has been devoted in this field, and those works can be roughly divided into two types — Multiple imputation and single imputation, where the latter can be further classified  ...  Imputation based on latent component-based approaches This type of method has a general procedure for reconstructing an incomplete data matrix.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5772/intechopen.94068">doi:10.5772/intechopen.94068</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2xlwn2da6vdxdhh7oikjpvzrdy">fatcat:2xlwn2da6vdxdhh7oikjpvzrdy</a> </span>
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Variational Gibbs inference for statistical model estimation from incomplete data [article]

Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann
<span title="2022-05-09">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We validate VGI on a set of synthetic and real-world estimation tasks, estimating important machine learning models such as VAEs and normalising flows from incomplete data.  ...  We address this gap by introducing variational Gibbs inference (VGI), a new general-purpose method to estimate the parameters of statistical models from incomplete data.  ...  Data Systems.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.13180v2">arXiv:2111.13180v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/t2b2wggc3nakxpwkapvvqj65gi">fatcat:t2b2wggc3nakxpwkapvvqj65gi</a> </span>
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EMFlow: Data Imputation in Latent Space via EM and Deep Flow Models [article]

Qi Ma, Sujit K. Ghosh
<span title="2021-06-09">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
High dimensional incomplete data can be found in a wide range of systems.  ...  Due to the fact that most of the data mining techniques and machine learning algorithms require complete observations, data imputation is vital for down-stream analysis.  ...  Related work Recently, the applications of deep generative models like Generative Adversarial Networks (GAN) [28] have been extended to the field of missing data imputation under the assumption of Missing  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.04804v1">arXiv:2106.04804v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3nnr2yjvqfdodc4ogmwkugi374">fatcat:3nnr2yjvqfdodc4ogmwkugi374</a> </span>
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Handling Missing Data with Graph Representation Learning [article]

Jiaxuan You, Xiaobai Ma, Daisy Yi Ding, Mykel Kochenderfer, Jure Leskovec
<span title="2020-10-30">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
downstream labels are learned directly from incomplete data.  ...  However, existing imputation models tend to have strong prior assumptions and cannot learn from downstream tasks, while models targeting label prediction often involve heuristics and can encounter scalability  ...  Acknowledgments We gratefully acknowledge the support of DARPA under Nos.  ... 
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