Papers Imputation
“Imputation” 태그가 달린 논문 1,237편 · 필터 해제
Missing value imputation with adversarial random forests -- MissARF
Handling missing values is a common challenge in biostatistical analyses, typically addressed by imputation methods. We propose a novel, fast, and easy-to-use imputation method called missing value imputation with advers…
Density EstimationImputationMissing ValuesMoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling
Recent years have witnessed a growing interest for time series foundation models, with a strong emphasis on the forecasting task. Yet, the crucial task of out-of-domain imputation of missing values remains largely undere…
Domain GeneralizationImputationMissing ValuesTime SeriesBMFM-DNA: A SNP-aware DNA foundation model to capture variant effects
Large language models (LLMs) trained on text demonstrated remarkable results on natural language processing (NLP) tasks. These models have been adapted to decipher the language of DNA, where sequences of nucleotides act …
ImputationPromoter DetectionLeveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests
Evaluating the abilities of learners is a fundamental objective in the field of education. In particular, there is an increasing need to assess higher-order abilities such as expressive skills and logical thinking. Const…
Data AugmentationImputationDIM-SUM: Dynamic IMputation for Smart Utility Management
Time series imputation models have traditionally been developed using complete datasets with artificial masking patterns to simulate missing values. However, in real-world infrastructure monitoring, practitioners often e…
ImputationManagementMissing ValuesTrustworthy Prediction with Gaussian Process Knowledge Scores
Probabilistic models are often used to make predictions in regions of the data space where no observations are available, but it is not always clear whether such predictions are well-informed by previously seen data. In …
Anomaly DetectionGPRImputationPredictionLSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation
Time series with missing or irregularly sampled data are a persistent challenge in machine learning. Many methods operate on the frequency-domain, relying on the Fast Fourier Transform (FFT) which assumes uniform samplin…
ImputationTime SeriesCovariance Decomposition for Distance Based Species Tree Estimation
In phylogenomics, species-tree methods must contend with two major sources of noise; stochastic gene-tree variation under the multispecies coalescent model (MSC) and finite-sequence substitutional noise. Fast agglomerati…
ImputationPeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
Accurate weather forecasts are essential for supporting a wide range of activities and decision-making processes, as well as mitigating the impacts of adverse weather events. While traditional numerical weather predictio…
Deep LearningGraph structure learningImputationTime Series ForecastingCross-Domain Conditional Diffusion Models for Time Series Imputation
Cross-domain time series imputation is an underexplored data-centric research task that presents significant challenges, particularly when the target domain suffers from high missing rates and domain shifts in temporal d…
DenoisingDomain AdaptationImputationMissing Values+2HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
Single-cell transcriptomics has become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and transcriptional regulation at the s…
Contrastive LearningImputationIMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples)
Missing data imputation in tabular datasets remains a pivotal challenge in data science and machine learning, particularly within socioeconomic research. However, real-world socioeconomic datasets are typically subject t…
Computational EfficiencyImputationFilling in the Blanks: Applying Data Imputation in incomplete Water Metering Data
In this work, we explore the application of recent data imputation techniques to enhance monitoring and management of water distribution networks using smart water meters, based on data derived from a real-world IoT wate…
ImputationManagementSchedulingA Probabilistic Framework for Imputing Genetic Distances in Spatiotemporal Pathogen Models
Pathogen genome data offers valuable structure for spatial models, but its utility is limited by incomplete sequencing coverage. We propose a probabilistic framework for inferring genetic distances between unsequenced ca…
ImputationIntegrated Analysis for Electronic Health Records with Structured and Sporadic Missingness
Objectives: We propose a novel imputation method tailored for Electronic Health Records (EHRs) with structured and sporadic missingness. Such missingness frequently arises in the integration of heterogeneous EHR datasets…
ImputationSTAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
Traffic data imputation is fundamentally important to support various applications in intelligent transportation systems such as traffic flow prediction. However, existing time-to-space sequential methods often fail to e…
Graph AttentionImputationMixture-of-ExpertsTraffic Data ImputationAddressing Correlated Latent Exogenous Variables in Debiased Recommender Systems
Recommendation systems (RS) aim to provide personalized content, but they face a challenge in unbiased learning due to selection bias, where users only interact with items they prefer. This bias leads to a distorted repr…
FairnessImputationRecommendation SystemsSelection biasFilling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion
Missing data in spatiotemporal systems presents a significant challenge for modern applications, ranging from environmental monitoring to urban traffic management. The integrity of spatiotemporal data often deteriorates …
ImputationLSM-2: Learning from Incomplete Wearable Sensor Data
Foundation models, a cornerstone of recent advancements in machine learning, have predominantly thrived on complete and well-structured data. Wearable sensor data frequently suffers from significant missingness, posing a…
DiagnosticImputationSelf-Supervised LearningDoes Prompt Design Impact Quality of Data Imputation by LLMs?
Generating realistic synthetic tabular data presents a critical challenge in machine learning. It adds another layer of complexity when this data contain class imbalance problems. This paper presents a novel token-aware …
Binary ClassificationImputationIn-Context LearningPrompt Engineering+1