SARGAN uses GANs to fill in missing radar frequency bands.
problem Missing spectral information in low frequency radar bands.
method Generative adversarial network (GAN) trained on paired original and missing band signals.
result Initial results show promise in recovering missing spectral information.
Method reconstructs missing wind farm data using graph theory and nearest neighbors.
problem Missing data in wind farm records due to sensor failures.
method Combines spectral graph theory and k-Nearest Neighbors to estimate missing data.
result Significant improvement in data reconstruction over existing methods.
Paper studies community detection in censored hypergraphs using information theory.
problem Community detection in censored hypergraphs with missing values.
method Information-theoretic approach, polynomial-time algorithm, spectral algorithm with refinement.
result Derives information-theoretic threshold for exact recovery of community structure.
Framework for robust matrix estimation with side information.
problem High-dimensional matrix estimation with restrictive structure.
method Flexible framework decomposing matrix into four components: interaction, row, column, and residual.
result Improved imputation accuracy and treatment-effect estimation with side information.
New method fits low-rank models for egocentrically sampled networks.
problem Statistical modeling of egocentrically sampled partial networks.
method Graph spectral properties-based approach for low-rank models.
result Consistent recovery of missing subnetworks for sparse networks.
Bayesian framework integrates spectral deconvolution with expert reasoning for robust peak estimation.
problem Challenges in extracting meaningful peaks from noisy or complex spectra.
method Bayesian spectral deconvolution coupled with a physical-property regression layer.
result Recovery of weak peaks in poly(lactic acid) IR spectra related to degradation rates.
New Fourier transform method handles missing data and asynchronous observations.
problem Volatility inference with missing or asynchronous data.
method Spectral framework using Fourier transforms.
result Consistent volatility functional estimation with limit distributions.
Bayesian model reconstructs time and frequency data robustly.
problem Missing observations and noise in time/frequency data.
method Probabilistic model, Bayesian update, joint reconstruction.
result Effective joint time/frequency reconstruction with missing data.
In the traditional framework of spectral learning of stochastic time series models, model parameters are estimated based on trajectories of fully recorded observations. However, real-world time series data often contain missing values, and worse, the distributions of missingness events over time are often not independe…
This paper focuses on obtaining clustering information about a distribution from its i.i.d. samples. We develop theoretical results to understand and use clustering information contained in the eigenvectors of data adjacency matrices based on a radial kernel function with a sufficiently fast tail decay. In particular, …
Algorithm learns graph ARMA processes for missing signal estimation.
problem Missing signal estimation in time-varying graph signals.
method Learning joint time-vertex power spectral density through convex relaxations.
result High accuracy in time-vertex signal estimation.
This paper tackles incomplete multi-view clustering with spectral perturbation theory.
problem Realistic clustering scenario where data instances are missing in certain views.
method Spectral perturbation theory and matrix completion method for incomplete similarity matrix.
result The minimization of perturbation risk bounds maximizes the final fusion result across all views.
New accelerators for EM improve convergence speed in complex mixture models.
problem Improving the convergence speed of the EM algorithm for complex mixture models.
method Derive a new operator connecting global descent and local convergence, and use it to develop two acceleration strategies.
result Two new acceleration strategies (G-Accelerator and Geo-Adaptive) significantly improve EM algorithm performance.
Paper tackles informative labels in semi-supervised learning, proposing debiasing methods.
problem Informative labels can bias semi-supervised learning models, especially when some classes are more likely to be labeled.
method Estimates missing-data mechanism and uses inverse propensity weighting to debias SSL algorithms.
result Proposed methods improve SSL performance, demonstrated on various datasets including medical ones.
Enhances TCK for missing data and incomplete labels in time series.
problem Missing data and incomplete labels in time series analysis.
method Ensemble learning with Bayesian mixture models, representation of missing patterns, semi-supervised learning.
result Improved accuracy in similarity learning for time series with missing and incomplete labels.
New ESGM scores include a 'Missing' pillar to account for unpublished ESG data.
problem Unpublished ESG data affects the reliability of ESG scores.
method Formulated a new 'Missing' pillar and introduced ESGM scores.
result ESGM scores improve risk assessment and avoid exclusion of assets.
New method handles missing data in multimodal brain imaging.
problem Missing data in multimodal brain imaging.
method Full Information Linked ICA (FI-LICA) algorithm.
result FI-LICA outperforms current practices in classification and prediction.
Detect anomalies in complex networks using topological subspace detectors.
problem Detect anomalies in complex networks defined by simplicial complexes.
method Formulate a hypothesis testing framework using Neyman-Pearson matched topological subspace detectors.
result Effective detection of anomalies in foreign currency exchange networks and other real-world data.
MIM adds indicator variables to improve model performance on incomplete data.
problem Missing data in incomplete data sets.
method Missing Indicator Method (MIM) and Selective MIM (SMIM).
result MIM improves model performance for informative missing values and high-dimensional data.
End-to-end model for time series classification with missing data.
problem Time series classification with missing data.
method End-to-end neural network that unifies imputation and representation learning.
result Proposed model outperforms state-of-the-art approaches for incomplete time series classification.
Spectral methods simplify data analysis, improving accuracy and stability.
problem Extracting meaningful information from noisy, incomplete data.
method Eigenvalues and eigenvectors of matrices constructed from data.
result Spectral methods are effective and can be analyzed using modern statistical theory.
Uncertainty principles such as Heisenberg's provide limits on the time-frequency concentration of a signal, and constitute an important theoretical tool for designing and evaluating linear signal transforms. Generalizations of such principles to the graph setting can inform dictionary design for graph signals, lead to …
Consistent supervised learning with missing values is possible using imputation or specialized models.
problem Predicting with missing values in both training and testing data.
method Two approaches: imputing with a constant and using a predictor for complete observations through multiple imputation. Decision trees can handle missing values naturally.
result Imputing with a constant can be consistent when missing values are not informative.
Machine learning helps predict smoke types for safer forest burns.
problem Determining which fuels to burn safely to reduce wildfire risk and minimize smoke.
method Machine learning techniques like spectral clustering and manifold learning.
result Interpretable representations and tools for differentiating smoke types.
Proposes a method to select features for subgroup datasets with systematic missing data.
problem Feature selection for datasets with subgroup structure and systematic missing data.
method Develops a heterogeneous graph neural network to propagate information between feature-subgroup-target variable connections.
result Demonstrates improved feature selection performance and scalability.
Proposes a Latent Block Model for analyzing missing data.
problem Missing data can lead to misleading conclusions if not properly accounted for.
method Co-clustering model based on Latent Block Model, with variational EM algorithm for inference and model selection criterion.
result The proposed model reveals meaningful groups and insights from non-voters in French Parliament voting records.
WGNN learns graph representations from incomplete attribute data.
problem Missing node attributes in graphs.
method WGNN learns node representations from decomposed attribute matrices and uses Wasserstein space for message passing.
result WGNN outperforms existing methods in node classification tasks with missing attribute data.
New model handles missing data effectively in autoregressive models.
problem Handling missing data in autoregressive models.
method Reinterpret existing models through missing data lens, introduce principled framework for incomplete datasets, active information acquisition.
result MO-ARM consistently outperforms imputation baselines across real-world benchmarks.
Quantile-Frequency Analysis detects nonlinear dynamics in financial time series.
problem Detecting nonlinear dynamics in financial time series models.
method Quantile periodogram and trigonometric quantile regression.
result QFA provides additional insights into financial time series models.
Trinary decision tree improves handling of missing data in machine learning.
problem Improving accuracy in decision tree algorithms when dealing with missing data.
method Introduces Trinary decision tree, which does not assume missing values contain information about the response.
result Trinary decision tree outperforms other algorithms in Missing Completely at Random settings, especially when data is only missing out-of-sample.
In this paper we present deterministic conditions for success of sparse subspace clustering (SSC) under missing data, when data is assumed to come from a Union of Subspaces (UoS) model. We consider two algorithms, which are variants of SSC with entry-wise zero-filling that differ in terms of the optimization problems u…
Missing data enhances privacy in differential privacy.
problem Privacy preservation in datasets with missing values.
method Formalized missing data as a privacy amplification mechanism within differential privacy.
result Incomplete data can yield privacy amplification for differentially private algorithms.
PC-GAIN improves GAIN's imputation by incorporating category information.
problem Missing data in incomplete datasets.
method Pre-training with pseudo-labels and incorporating an auxiliary classifier into GAIN.
result Significantly improved imputation quality compared to GAIN.
New framework predicts time series with missing values without imputation.
problem Predicting time series with missing values, especially when there's no ground truth for missing data.
method CRIB framework, combining attention mechanism and consistency regularization.
result CRIB framework predicts accurately even under high missing rates.
CVIB uses information theory to learn counterfactuals from MNAR data without RCTs.
problem Debiasing learning from missing-not-at-random (MNAR) data in recommendation systems.
method CVIB, a variational information bottleneck, separates task-aware mutual information into factual and counterfactual parts.
result CVIB significantly enhances both shallow and deep models in recommendation systems.
Graph convolutional networks fail to use eigenvectors beyond the first, unlike spectral embedding.
problem Understanding when graph convolutional networks fail compared to spectral embedding.
method Presented a simple generative model to illustrate failure.
result Graph convolutional networks fail to use eigenvectors beyond the first in certain graphs.
New model learns from missing modalities and class labels.
problem Conflict between learning joint representations and modalities in multi-modal data.
method Introduces a novel conditional multi-modal discriminative model using an informative prior distribution and a likelihood-free objective function.
result Our model achieves state-of-the-art results in downstream classification, acoustic inversion, and image and annotation generation.
Imputation with missing-indicators improves machine learning performance, especially for categorical attributes.
problem Missing values in datasets can be lost through imputation, potentially reducing model performance.
method Combines missing-indicator approach with imputation strategies on real datasets.
result Missing-indicators generally increase classification performance, especially for categorical attributes.
New algorithms handle missing data to improve fairness in machine learning.
problem Missing values in data can lead to unfair outcomes in machine learning models.
method Developed scalable and adaptive algorithms to handle missing values while preserving predictive information.
result Our adaptive algorithms consistently achieve higher fairness and accuracy than standard impute-then-classify methods.
Real music signals are highly variable, yet they have strong statistical structure. Prior information about the underlying physical mechanisms by which sounds are generated and rules by which complex sound structure is constructed (notes, chords, a complete musical score), can be naturally unified using Bayesian modell…
Spectral clustering performance depends on eigenvector fluctuations, shown to be Gaussian.
problem Predicting the performance of spectral clustering.
method General spike random matrix model and rotational invariance of noise.
result Fluctuations of eigenvector entries are Gaussian in large-dimensional regime.
Paper proposes method to recover quantized data with missing info.
problem Recovering quantized data with missing information.
method Regularized convex cost function with Bi-factorization and Augmented Lagrangian Method.
result The method finds global minimizer of the cost function.
Paper proposes a novel method to reduce mutual information for missing data imputation.
problem Missing data imputation in datasets with missingness patterns.
method Iterative minimization of KL divergence between imputed data and missingness mask, using rectified flow training objective.
result The proposed method achieves superior imputation performance on synthetic and real-world datasets.
Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data
problem Improving prediction intervals for non-exchangeable time-indexed datasets
method Spectral adaptive conformal prediction
result Improves on fixed spectral weighting while monitoring uncertainty changes
GT estimator shows convergence for Markov samples, improving i.i.d. results.
problem Estimating missing mass in Markov samples.
method Analyzed convergence of Good-Turing estimator for Markov samples, considering spectral properties of transition matrices.
result The convergence of the GT estimator for Markov samples depends on the spectral properties of the transition matrices, leading to a new minimax rate of 1/(nβ5) for rank-2 Markov chains. A new criterion HBIC improves model selection for factor analysis with missing data.
problem Model selection for factor analysis with incomplete data.
method Proposes a novel criterion HBIC that uses actual observed information in the penalty term.
result HBIC is more accurate than BIC when missing data rates are high.
Spectral learning extends matrix methods to tensors for better latent variable modeling.
problem Limitations of matrix-based spectral methods in capturing non-Gaussian data.
method Extend spectral decomposition to tensor-based methods for higher-order moments.
result Tensor decomposition can identify latent effects missed by matrix methods.
Semi-supervised learning benefits from informative missing labels, improving classifier performance.
problem Missing labels in semi-supervised learning can be informative, improving classifier performance.
method Formulates missingness as a mixture model problem and uses EM algorithm for fitting.
result Modelling informative missingness can yield a classifier with smaller expected error than a completely labelled sample.