{"total":32,"rows":[{"id":"2506.05321","title":"LSM-2: Learning from Incomplete Wearable Sensor Data","created":"2025-06-05","cited_by_count":23,"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":"2410.13203","title":"TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering","created":"2024-10-17","cited_by_count":5,"area":null},{"id":"2410.21523","title":"Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data","created":"2024-10-28","cited_by_count":4,"area":null},{"id":"2506.02306","title":"CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation","created":"2025-06-02","cited_by_count":4,"area":null},{"id":"2510.02625","title":"One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data","created":"2025-10-03","cited_by_count":4,"area":null},{"id":"2503.13057","title":"MaskSDM with Shapley values to improve flexibility, robustness, and explainability in species distribution modeling","created":"2025-03-17","cited_by_count":3,"area":null},{"id":"2501.02648","title":"Representation Learning of Lab Values via Masked AutoEncoders","created":"2025-01-05","cited_by_count":2,"area":null},{"id":"2510.09513","title":"Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Data","created":"2025-10-10","cited_by_count":2,"area":null},{"id":"2410.01933","title":"TAEGAN: Generating Synthetic Tabular Data For Data Augmentation","created":"2024-10-02","cited_by_count":1,"area":null},{"id":"2504.14610","title":"Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformer","created":"2025-04-20","cited_by_count":1,"area":null},{"id":"2507.23676","title":"DepMicroDiff: Diffusion-Based Dependency-Aware Multimodal Imputation for Microbiome Data","created":"2025-07-31","cited_by_count":1,"area":null},{"id":"2410.08794","title":"M$^3$-Impute: Mask-guided Representation Learning for Missing Value Imputation","created":"2024-10-11","cited_by_count":0,"area":null},{"id":"2506.08844","title":"IMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples)","created":"2025-06-10","cited_by_count":0,"area":null},{"id":"2509.03316","title":"Meta-Imputation Balanced (MIB): An Ensemble Approach for Handling Missing Data in Biomedical Machine Learning","created":"2025-09-03","cited_by_count":0,"area":null},{"id":"2511.01196","title":"An Interdisciplinary and Cross-Task Review on Missing Data Imputation","created":"2025-11-03","cited_by_count":0,"area":null},{"id":"2511.19535","title":"Masked Autoencoder Joint Learning for Robust Spitzoid Tumor Classification","created":"2025-11-24","cited_by_count":0,"area":null},{"id":"2512.14221","title":"Weighted Conformal Prediction Provides Adaptive and Valid Mask-Conditional Coverage for General Missing Data Mechanisms","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2601.18500","title":"Nearly Optimal Bayesian Inference for Structural Missingness","created":"2026-01-26","cited_by_count":0,"area":null},{"id":"2602.07875","title":"Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion","created":"2026-02-08","cited_by_count":0,"area":null},{"id":"2602.15159","title":"Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding","created":"2026-02-16","cited_by_count":0,"area":null},{"id":"2606.00656","title":"Demystifying the Optimal Fair Classifier in Multi-Class Classification","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2606.05073","title":"Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.05361","title":"TabSODA: Tabular Diffusion based Imputation with Skip Pattern Detection and Ordinal Awareness","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2607.05613","title":"SafeImpute: Reliable Clinical Data Imputation via Conformal Selection","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2607.06930","title":"Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2608.09030","title":"Closing the loop in learning with missing data","created":"2026-08-10","cited_by_count":0,"area":null},{"id":"2609.28105","title":"Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingness","created":"2026-09-23","cited_by_count":0,"area":null},{"id":"2609.37664","title":"Learning Causal Normalizing Flows from Incomplete Data via Observed-Data Likelihood","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.12379","title":"Marformer: A Transformer for Predicting Missing Data Distributions","created":"2026-10-08","cited_by_count":0,"area":null},{"id":"2610.15699","title":"TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks","created":"2026-10-11","cited_by_count":0,"area":null},{"id":"2610.20342","title":"When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}