Develops an efficient k-means algorithm for clustering incomplete datasets.
problem Clustering datasets with missing values.
method Introduces km-means algorithm that handles incomplete records. result Efficacy demonstrated in various settings and patterns of missing data.
Efficiently clusters incomplete data without imputation or full EM, faster and more accurate.
problem Clustering partially recorded data efficiently.
method Model-based approach using multivariate t-distributions, considering only observed values.
result Approach is more accurate and computationally efficient than alternatives.
Graph-based method predicts business conduct risk from incomplete data.
problem Sparse and biased data limits risk assessment.
method Visibility-aware GCNII framework on corporate graph.
result Graph-based approach outperforms non-graph methods in predicting future incidents.
DNI recovers missing brain data from corrupted recordings.
problem Corrupted neural recordings from multielectrode systems.
method Deep Neural Imputation framework using autoencoders.
result DNI recovers both time series and frequency content from corrupted data.
Improves seq2seq speech recognition by addressing overconfidence and incomplete transcriptions.
problem Overconfidence and incomplete transcriptions in seq2seq speech recognition.
method Proposed practical solutions to address overconfidence and incomplete transcriptions using a trigram language model.
result Achieved competitive speaker independent word error rates (6.7%) with a trigram language model.
Proposes a matrix completion method for medical records with long time intervals.
problem Incomplete medical records due to long time intervals between patient visits.
method Decomposes a matrix with missing data into latent factors with locally linear constraint.
result The proposed algorithm achieves the best performance compared to existing methods.
Estimates network structure from incomplete event data.
problem Estimating network structure from incomplete event data.
method Developed a novel approach using an unbiased estimator of the complete data log-likelihood function.
result Proposed a computationally efficient estimation algorithm.
Develops a new model for generating counterfactuals using incomplete data.
problem Lack of complete labels and data in medical image analysis.
method Semi-supervised deep causal generative model that infers missing values using causal inference.
result Generates realistic counterfactuals even with incomplete labels.
Paper presents a method for imputing and forecasting structural response from incomplete sensor data.
problem Missing sensor data in structural health monitoring (SHM).
method Incremental Bayesian tensor learning for spatiotemporal missing data reconstruction and forecasting.
result The proposed method achieves accurate and robust imputation and prediction even with high rates of missing data.
CART can bias propensity score estimates with missing data, but multiple imputation is better.
problem Bias in propensity score estimation with CART and missing data.
method Examined CART performance with different approaches to missing data: direct CART, complete case analysis, and multiple imputation.
result Multiple imputation followed by CART outperformed direct CART with missing data.
Probabilistic methods improve SHM by learning from noisy, incomplete data.
problem Noisy and incomplete SHM data, lack of prior labels.
method Probabilistic algorithms for semi-supervised, active, and multi-task learning.
result Probabilistic methods enhance SHM by incorporating new data.
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.
Model predicts unseen climate extremes to inform risk planning.
problem Missing unseen climate extremes in historical records.
method DeepX-GAN model capturing spatial dependence.
result Unseen heat extremes disproportionately threaten vulnerable regions.
This paper introduces modal epistemic tools for risk management.
problem Identifying and certifying risk claims when institutions lack the necessary epistemic stance.
method Develops crisp and fuzzy modal semantics for assurance and working commitment, distinguishing between object-level risk claims and meta-level epistemic diagnostics.
result Risk governance should model evidential incompleteness and failures of escalation, not just hazards and losses.
New method clusters strong and weak views effectively, improving performance by up to 40%.
problem Clustering incomplete multi-view data with unbalanced incompleteness.
method View evolution scheme and weighted multi-view subspace clustering.
result Improves clustering performance by up to 40% on three metrics.
Measures incompleteness of financial markets using asset rank and acceptance set dimension.
problem Measuring incompleteness of incomplete financial markets.
method Introduce rank of vector price process and dimension of acceptance set.
result Rank and dimension of acceptance set are equal.
Optimal consumption strategy in incomplete markets identified.
problem Optimal consumption of multiple goods in incomplete semimartingale markets.
method Formulated dual problem, identified existence and uniqueness conditions, characterized optimal strategy.
result Characterization of optimal consumption strategy in terms of dual optimizer.
The noncompact Yamabe flow can lead to incomplete metrics over infinite time.
problem Incompleteness of noncompact Yamabe flow solutions over infinite time.
method Analysis of long-time behavior of the noncompact Yamabe flow.
result Existence of a long-time solution that is complete for each time but converges to an incomplete metric.
New flow preserves singularities on incomplete manifolds.
problem Evolve incomplete manifolds with bounded curvature.
method Construct Ricci de Turck flow uniformly equivalent to initial metric.
result Any incomplete manifold can be evolved for a short time.
Study shows IMP gluing spacetimes are incomplete.
problem Local geometry and completeness of IMP gluing spacetimes.
method Investigation of IMP gluing initial data sets, existence of outer trapped surfaces, and application of Penrose's incompleteness theorem.
result Implication of null incompleteness for IMP gluing spacetimes.
The possibility of statistical evaluation of the market completeness and incompleteness is investigated for continuous time diffusion stock market models. It is known that the market completeness is not a robust property: small random deviations of the coefficients convert a complete market model into a incomplete one.…
Buy-and-hold strategy optimal for incomplete markets.
problem Optimal super-replication of Markovian claims in fully incomplete markets.
method Analyzes fully incomplete markets with stochastic volatility and rough volatility models.
result Super-replication of Markovian claims is of buy-and-hold type in fully incomplete markets.
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.
Develops GNNs for incomplete graphs, improving learning from missing node attributes.
problem Learning from incomplete graphs with missing node attributes.
method Introduces PaGNNs with novel partial aggregation functions for incomplete graph data.
result Demonstrates effectiveness and efficiency of PaGNNs on various datasets.
This paper solves hedging in incomplete markets using neural networks.
problem Hedging in incomplete markets with risk factor, illiquidity, and discrete transaction dates.
method Proposes a jump-diffusion model and uses RNN, LSTM, and Mogrifier-LSTM neural networks for hedging strategies.
result Mogrifier-LSTM is the fastest and most effective model for hedging.
WEST uses EHRs and expert cases to improve rare disease phenotyping.
problem Limited labeled data for rare diseases.
method Weakly supervised transformer model trained on probabilistic silver-standard labels.
result WEST outperforms existing methods in phenotype classification and subphenotyping.
PCKID kernel improves spectral clustering on incomplete data.
problem Handling incomplete data in spectral clustering.
method Combining posterior distributions of Gaussian Mixture Models on different scales.
result PCKID kernel outperforms baseline methods for all fractions of missing values.
Methodology extrapolates wind fields from sparse data with uncertainty quantification.
problem Extrapolating wind fields from limited measurements with uncertainty.
method Nonparametric Bayesian dictionary learning for sparse/incomplete data.
result Enhanced extrapolation accuracy, even in high-dimensional data.
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.
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records (EHRs) provide an alternative to the longitudinal cohort studies traditio…
We investigate the possibility of statistical evaluation of the market completeness for discrete time stock market models. It is known that the market completeness is not a robust property: small random deviations of the coefficients convert a complete market model into a incomplete one. The paper shows that market inc…
Researchers analyze record statistics in correlated random walks and Lévy flights.
problem Understanding record statistics in correlated time series.
method Review of random walk models and Lévy flights, focusing on number of records and record ages.
result Effects of correlations on record statistics were observed and analyzed.
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.
Paper proposes HI-VAE for handling incomplete heterogeneous data.
problem Handling incomplete and heterogeneous data using VAEs.
method Proposes HI-VAE framework for fitting real-valued, positive real valued, interval, categorical, ordinal and count data.
result HI-VAE outperforms supervised models trained on incomplete data.
The paper proves local and long-term existence of Ricci de Turck flow on incomplete edge manifolds.
problem Proving existence of Ricci de Turck flow on incomplete edge manifolds.
method Careful analysis of the Lichnerowicz Laplacian and the Ricci de Turck flow equation.
result Local and long-term existence of Ricci de Turck flow on incomplete edge manifolds.
In the setting of exponential investors and uncertainty governed by Brownian motions we first prove the existence of an incomplete equilibrium for a general class of models. We then introduce a tractable class of exponential-quadratic models and prove that the corresponding incomplete equilibrium is characterized by a …
Algorithm recovers sparse PCA support from incomplete data.
problem Sparse PCA with incomplete and noisy data.
method Semidefinite program (SDP) relaxation of non-convex l1-regularized PCA. result SDP enables exact recovery of true support of sparse leading eigenvector.
Study Dirac operators on incomplete cusp edge spaces, proving self-adjointness and Fredholm properties.
problem Analyzing Dirac operators on complex geometric spaces.
method Construct heat kernel, prove self-adjointness and Fredholm properties, establish index formula.
result Proved Dirac operators are essentially self-adjoint and Fredholm.
Global existence of equilibrium in incomplete market with stochastic annuity.
problem Proving existence of equilibrium in an incomplete market with stochastic annuity.
method Characterized by fully coupled quadratic backward stochastic differential equations, existence proved under Markovian assumptions.
result Global existence of an incomplete, continuous-time finite-agent Radner equilibrium.
New ML method detects incomplete bid-rigging cartels.
problem Detecting incomplete bid-rigging cartels in competitive bidding.
method Combines statistical screens with machine learning.
result Algorithm outperforms existing methods in incomplete cartels.
The study sets performance limits for record linkage using KL divergence.
problem Efficiently merging records in large, noisy databases to remove duplicates.
method Assesses performance bounds using Kullback-Leibler divergence in a Bayesian record linkage framework.
result Provides upper and lower bounds on misclassification probability.
Ancestry improves genealogy search results by ranking diverse record types.
problem Ranking diverse genealogy records equitably from various sources.
method Customized Coordinate Ascent, Stochastic Search, Normalized Cumulative Entropy.
result Demonstrated effectiveness of algorithms in improving relevance and diversity.
The paper extends cost-efficiency analysis to incomplete markets.
problem Cost-efficiency in incomplete financial markets.
method Extends results from complete markets to incomplete markets, introduces new preferences.
result Optimal portfolios in non-decreasing preferences are perfectly cost-efficient.
New estimator for symmetric kernel expectations, robust to missing data.
problem Efficient estimation of symmetric kernel expectations with missing data.
method Median-of-Incomplete-U-Statistics (MIU) estimator.
result Established finite-sample concentration rate for MIU.
New spacetimes without CMC Cauchy surfaces are shown to be incomplete.
problem Vacuum cosmological spacetimes without constant mean curvature Cauchy surfaces.
method Construction and analysis of specific spacetimes with large gluing parameters.
result These spacetimes are shown to be null geodesically incomplete.
Paper improves dictionary learning from incomplete data.
problem Learning dictionaries from incomplete or corrupted data.
method Adapts ITKrM algorithm to incomplete/masked data, incorporating low-rank components.
result ITKrMM outperforms existing methods in terms of speed and reconstruction quality.
Bayesian approach uses node attributes to improve incomplete relational data.
problem Improving community detection and link prediction in incomplete relational data.
method Bayesian probabilistic approach incorporating binary node attributes for both directed and undirected networks, using efficient Gibbs sampling.
result State-of-the-art link prediction results, especially with highly incomplete data.
Study optimal investment and consumption in incomplete markets with nonlinear expectations.
problem Utility maximization in incomplete markets with general constraints.
method Utilizes g-martingale method to solve optimization problem for various utility functions. result Characterizes optimal investment-consumption strategy through quadratic BSDE solutions.