{"total":305,"rows":[{"id":"2202.08516","title":"SAITS: Self-Attention-based Imputation for Time Series","created":"2022-02-17","cited_by_count":169,"area":null},{"id":"2307.03759","title":"A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection","created":"2023-07-07","cited_by_count":145,"area":null},{"id":"2202.00449","title":"A Consistent and Efficient Evaluation Strategy for Attribution Methods","created":"2022-02-01","cited_by_count":102,"area":null},{"id":"2108.00298","title":"Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks","created":"2021-07-31","cited_by_count":95,"area":null},{"id":"2206.07769","title":"HyperImpute: Generalized Iterative Imputation with Automatic Model Selection","created":"2022-06-15","cited_by_count":83,"area":null},{"id":"2107.10483","title":"Efficient Neural Causal Discovery without Acyclicity Constraints","created":"2021-07-22","cited_by_count":82,"area":null},{"id":"2204.04875","title":"Learning to Induce Causal Structure","created":"2022-04-11","cited_by_count":68,"area":null},{"id":"2205.13479","title":"Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations","created":"2022-05-26","cited_by_count":64,"area":null},{"id":"2111.12128","title":"On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features","created":"2021-11-23","cited_by_count":63,"area":null},{"id":"2106.00311","title":"What's a good imputation to predict with missing values?","created":"2021-06-01","cited_by_count":62,"area":null},{"id":"2309.09968","title":"Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees","created":"2023-09-18","cited_by_count":62,"area":null},{"id":"2305.09696","title":"Generative Table Pre-training Empowers Models for Tabular Prediction","created":"2023-05-16","cited_by_count":55,"area":null},{"id":"2206.15306","title":"Transfer Learning with Deep Tabular Models","created":"2022-06-30","cited_by_count":52,"area":null},{"id":"2111.03187","title":"MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms","created":"2021-11-04","cited_by_count":51,"area":null},{"id":"2312.01728","title":"ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation","created":"2023-12-04","cited_by_count":50,"area":null},{"id":"2112.12047","title":"Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications","created":"2021-12-22","cited_by_count":47,"area":null},{"id":"2303.16005","title":"Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction","created":"2023-03-28","cited_by_count":47,"area":null},{"id":"2305.05890","title":"CUTS+: High-dimensional Causal Discovery from Irregular Time-series","created":"2023-05-10","cited_by_count":41,"area":null},{"id":"2309.13793","title":"ReMasker: Imputing Tabular Data with Masked Autoencoding","created":"2023-09-25","cited_by_count":37,"area":null},{"id":"2311.14332","title":"GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation","created":"2023-11-24","cited_by_count":35,"area":null},{"id":"2308.16361","title":"Large Language Models as Data Preprocessors","created":"2023-08-30","cited_by_count":34,"area":null},{"id":"2305.18612","title":"Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders","created":"2023-05-29","cited_by_count":32,"area":null},{"id":"2407.07582","title":"TIP: Tabular-Image Pre-training for Multimodal Classification with Incomplete Data","created":"2024-07-10","cited_by_count":32,"area":null},{"id":"2302.10363","title":"Transformed Distribution Matching for Missing Value Imputation","created":"2023-02-20","cited_by_count":31,"area":null},{"id":"2110.14708","title":"Identifiable Generative Models for Missing Not at Random Data Imputation","created":"2021-10-27","cited_by_count":30,"area":null},{"id":"2109.12144","title":"Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging","created":"2021-09-24","cited_by_count":27,"area":null},{"id":"2310.15978","title":"Graph Deep Learning for Time Series Forecasting","created":"2023-10-24","cited_by_count":26,"area":null},{"id":"2102.03340","title":"NRTSI: Non-Recurrent Time Series Imputation","created":"2021-02-05","cited_by_count":25,"area":null},{"id":"2410.21520","title":"LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation","created":"2024-10-28","cited_by_count":25,"area":null},{"id":"2112.04899","title":"Assessing Fairness in the Presence of Missing Data","created":"2021-12-07","cited_by_count":24,"area":null},{"id":"2304.01429","title":"Learning from data with structured missingness","created":"2023-04-04","cited_by_count":24,"area":null},{"id":"2506.05321","title":"LSM-2: Learning from Incomplete Wearable Sensor Data","created":"2025-06-05","cited_by_count":23,"area":null},{"id":"2102.04426","title":"Arbitrary Conditional Distributions with Energy","created":"2021-02-08","cited_by_count":22,"area":null},{"id":"2205.13869","title":"MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models","created":"2022-05-27","cited_by_count":22,"area":null},{"id":"2206.08478","title":"Classification of datasets with imputed missing values: does imputation quality matter?","created":"2022-06-16","cited_by_count":22,"area":null},{"id":"2312.01678","title":"Jellyfish: A Large Language Model for Data Preprocessing","created":"2023-12-04","cited_by_count":20,"area":null},{"id":"2405.11333","title":"GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing","created":"2024-05-18","cited_by_count":20,"area":null},{"id":"2111.13507","title":"Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features","created":"2021-11-26","cited_by_count":19,"area":null},{"id":"2211.09259","title":"The Missing Indicator Method: From Low to High Dimensions","created":"2022-11-16","cited_by_count":19,"area":null},{"id":"2201.10035","title":"Maximizing information from chemical engineering data sets: Applications to machine learning","created":"2022-01-25","cited_by_count":17,"area":null},{"id":"2110.15528","title":"Deconvolutional Networks on Graph Data","created":"2021-10-29","cited_by_count":16,"area":null},{"id":"2112.11507","title":"Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems","created":"2021-12-21","cited_by_count":15,"area":null},{"id":"2212.02810","title":"Data Imputation with Iterative Graph Reconstruction","created":"2022-12-06","cited_by_count":15,"area":null},{"id":"2305.09536","title":"A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them","created":"2023-05-16","cited_by_count":15,"area":null},{"id":"2407.21523","title":"Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI","created":"2024-07-31","cited_by_count":15,"area":null},{"id":"2211.05764","title":"DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems","created":"2022-11-09","cited_by_count":14,"area":null},{"id":"2403.11395","title":"Automated data processing and feature engineering for deep learning and big data applications: a survey","created":"2024-03-18","cited_by_count":14,"area":null},{"id":"2203.07018","title":"A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources","created":"2022-03-14","cited_by_count":13,"area":null},{"id":"2407.19804","title":"Imputation for prediction: beware of diminishing returns","created":"2024-07-29","cited_by_count":13,"area":null},{"id":"2208.06646","title":"Modeling Network-level Traffic Flow Transitions on Sparse Data","created":"2022-08-13","cited_by_count":12,"area":null},{"id":"2211.16771","title":"Handling Missing Data via Max-Entropy Regularized Graph Autoencoder","created":"2022-11-30","cited_by_count":12,"area":null},{"id":"2310.18688","title":"Clairvoyance: A Pipeline Toolkit for Medical Time Series","created":"2023-10-28","cited_by_count":12,"area":null},{"id":"2501.02191","title":"On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passing","created":"2025-01-04","cited_by_count":11,"area":null},{"id":"2102.12679","title":"Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data","created":"2021-02-25","cited_by_count":10,"area":null},{"id":"2308.14906","title":"BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition","created":"2023-08-28","cited_by_count":10,"area":null},{"id":"2407.09096","title":"STD-PLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal Data with PLM","created":"2024-07-12","cited_by_count":10,"area":null},{"id":"2408.11063","title":"Tabular Transfer Learning via Prompting LLMs","created":"2024-08-09","cited_by_count":10,"area":null},{"id":"2412.12886","title":"TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysis","created":"2024-12-17","cited_by_count":10,"area":null},{"id":"2103.08323","title":"Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation","created":"2021-03-12","cited_by_count":9,"area":null},{"id":"2109.08357","title":"Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns","created":"2021-09-17","cited_by_count":9,"area":null},{"id":"2202.02096","title":"To Impute or not to Impute? Missing Data in Treatment Effect Estimation","created":"2022-02-04","cited_by_count":9,"area":null},{"id":"2209.09239","title":"Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey","created":"2022-09-17","cited_by_count":9,"area":null},{"id":"2211.11596","title":"Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks","created":"2022-11-21","cited_by_count":9,"area":null},{"id":"2106.16057","title":"DAEMA: Denoising Autoencoder with Mask Attention","created":"2021-06-30","cited_by_count":8,"area":null},{"id":"2110.12002","title":"Fairness in Missing Data Imputation","created":"2021-10-22","cited_by_count":8,"area":null},{"id":"2305.15577","title":"Minimizing $f$-Divergences by Interpolating Velocity Fields","created":"2023-05-24","cited_by_count":8,"area":null},{"id":"2309.15529","title":"Missing-modality Enabled Multi-modal Fusion Architecture for Medical Data","created":"2023-09-27","cited_by_count":8,"area":null},{"id":"2402.12598","title":"Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations","created":"2024-02-19","cited_by_count":8,"area":null},{"id":"2403.02372","title":"OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport","created":"2024-03-04","cited_by_count":8,"area":null},{"id":"2506.00557","title":"Score Matching With Missing Data","created":"2025-05-31","cited_by_count":8,"area":null},{"id":"2103.09316","title":"Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison","created":"2021-03-14","cited_by_count":7,"area":null},{"id":"2107.01983","title":"Gradient Importance Learning for Incomplete Observations","created":"2021-07-05","cited_by_count":7,"area":null},{"id":"2201.11205","title":"Generative Trees: Adversarial and Copycat","created":"2022-01-26","cited_by_count":7,"area":null},{"id":"2207.01269","title":"DiffML: End-to-end Differentiable ML Pipelines","created":"2022-07-04","cited_by_count":7,"area":null},{"id":"2210.10446","title":"EGG-GAE: scalable graph neural networks for tabular data imputation","created":"2022-10-19","cited_by_count":7,"area":null},{"id":"2404.04905","title":"Review for Handling Missing Data with special missing mechanism","created":"2024-04-07","cited_by_count":7,"area":null},{"id":"2409.02143","title":"MLOmics: Cancer Multi-Omics Database for Machine Learning","created":"2024-09-02","cited_by_count":7,"area":null},{"id":"2106.04804","title":"EMFlow: Data Imputation in Latent Space via EM and Deep Flow Models","created":"2021-06-09","cited_by_count":6,"area":null},{"id":"2304.11749","title":"Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?","created":"2023-04-23","cited_by_count":6,"area":null},{"id":"2312.16581","title":"Continuous-time Autoencoders for Regular and Irregular Time Series Imputation","created":"2023-12-27","cited_by_count":6,"area":null},{"id":"2402.15255","title":"Optimal Transport for Structure Learning Under Missing Data","created":"2024-02-23","cited_by_count":6,"area":null},{"id":"2407.03836","title":"ADAPT: Multimodal Learning for Detecting Physiological Changes under Missing Modalities","created":"2024-07-04","cited_by_count":6,"area":null},{"id":"2503.02899","title":"OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels","created":"2025-03-03","cited_by_count":6,"area":null},{"id":"2107.11882","title":"Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective","created":"2021-07-25","cited_by_count":5,"area":null},{"id":"2108.12159","title":"Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework","created":"2021-08-27","cited_by_count":5,"area":null},{"id":"2110.00449","title":"Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference","created":"2021-10-01","cited_by_count":5,"area":null},{"id":"2201.03202","title":"Differentiable and Scalable Generative Adversarial Models for Data Imputation","created":"2022-01-10","cited_by_count":5,"area":null},{"id":"2204.04648","title":"Gaussian Processes for Missing Value Imputation","created":"2022-04-10","cited_by_count":5,"area":null},{"id":"2208.06573","title":"GEDI: A Graph-based End-to-end Data Imputation Framework","created":"2022-08-13","cited_by_count":5,"area":null},{"id":"2305.06044","title":"Correlation visualization under missing values: a comparison between imputation and direct parameter estimation methods","created":"2023-05-10","cited_by_count":5,"area":null},{"id":"2405.07233","title":"OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning","created":"2024-05-12","cited_by_count":5,"area":null},{"id":"2405.17712","title":"A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Models","created":"2024-05-28","cited_by_count":5,"area":null},{"id":"2407.00411","title":"Explainability of Machine Learning Models under Missing Data","created":"2024-06-29","cited_by_count":5,"area":null},{"id":"2409.09930","title":"Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series","created":"2024-09-16","cited_by_count":5,"area":null},{"id":"2503.01287","title":"Robust Simulation-Based Inference under Missing Data via Neural Processes","created":"2025-03-03","cited_by_count":5,"area":null},{"id":"2509.13778","title":"Imputation-Powered Inference","created":"2025-09-17","cited_by_count":5,"area":null},{"id":"2103.07206","title":"Medical data wrangling with sequential variational autoencoders","created":"2021-03-12","cited_by_count":4,"area":null},{"id":"2105.04655","title":"Causal Inference in medicine and in health policy, a summary","created":"2021-05-10","cited_by_count":4,"area":null},{"id":"2108.01701","title":"Categorical EHR Imputation with Generative Adversarial Nets","created":"2021-08-03","cited_by_count":4,"area":null},{"id":"2110.14010","title":"MisConv: Convolutional Neural Networks for Missing Data","created":"2021-10-26","cited_by_count":4,"area":null}],"next_cursor":"100"}