No regularization needed for InLDL, achieving efficient and effective model.
problem InLDL struggles with performance degradation due to missing degrees.
method Proposes a model that uses label distribution as a prior, implicitly regularizing the learning process.
result Achieves competitive performance without explicit regularization.
Generative adversarial networks (GANs) have shown great success in applications such as image generation and inpainting. However, they typically require large datasets, which are often not available, especially in the context of prediction tasks such as image segmentation that require labels. Therefore, methods such as…
Acquiring ground truth labels for unlabelled data can be a costly procedure, since it often requires manual labour that is error-prone. Consequently, the available amount of labelled data is increasingly reduced due to the limitations of manual data labelling. It is possible to increase the amount of labelled data samp…
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.
The paper tackles fairness in machine learning by modeling latent unbiased labels.
problem Ensuring fairness in machine learning systems that use biased data.
method Explicitly models a latent variable representing a hidden, unbiased label to achieve demographic parity.
result The latent variable approach successfully retrieves fair labels from biased data.
Improved VAE estimation from incomplete data using variational mixtures.
problem Estimating VAEs from incomplete data increases posterior complexity.
method Introducing variational mixtures based on finite and imputation distributions.
result Variational mixtures improve VAE estimation accuracy from incomplete data.
In a standard multi-output classification scenario, both features and labels of training data are partially observed. This challenging issue is widely witnessed due to sensor or database failures, crowd-sourcing and noisy communication channels in industrial data analytic services. Classic methods for handling multi-ou…
We propose the Limited Multi-Label (LML) projection layer as a new primitive operation for end-to-end learning systems. The LML layer provides a probabilistic way of modeling multi-label predictions limited to having exactly k labels. We derive efficient forward and backward passes for this layer and show how the layer…
Robustly estimates mean in incomplete data with corrupted examples.
problem Estimating mean in data with missing values and outliers.
method Algorithms for robust estimation with optimal error guarantees in nearly-linear time.
result Information-theoretically optimal error guarantees for mean estimation.
Paper derives best- and worst-case GlueVaR measures with incomplete data.
problem Risk measurement with limited information and shape constraints.
method Unified framework based on partial distribution information and shape properties.
result Characterization of extremal GlueVaR distributions with convex envelopes.
In this paper, we propose PCKID, a novel, robust, kernel function for spectral clustering, specifically designed to handle incomplete data. By combining posterior distributions of Gaussian Mixture Models for incomplete data on different scales, we are able to learn a kernel for incomplete data that does not depend on a…
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 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.
Unified framework for structured prediction with partial labelling.
problem Learning with partial labelling costs less but is not well studied.
method Structured prediction and infimum loss for a wide range of problems.
result Unified framework leads to explicit algorithms with statistical consistency.
Unified approach for learning with weak labels across various tasks.
problem Learning with noisy or incomplete labels in diverse machine learning settings.
method Implicit posterior models for joint label inference.
result Unified training objective for various machine learning tasks.
This paper compares rank aggregation methods for partial label ranking.
problem Handling partial label ranking with ties.
method Scoring-based and non-parametric probabilistic-based rank aggregation methods.
result Scoring-based variants consistently outperform the state-of-the-art method.
Paper proposes an algorithm to recover full supervision from weakly labeled data.
problem Machine learning requires expensive data annotation, motivating the use of weak supervision.
method The paper introduces a disambiguation principle and an empirical disambiguation algorithm for partial labelling.
result The algorithm achieves exponential convergence rates under learnability assumptions.
In conventional supervised learning, a training dataset is given with ground-truth labels from a known label set, and the learned model will classify unseen instances to known labels. This paper studies a new problem setting in which there are unknown classes in the training data misperceived as other labels, and thus …
We derive asset pricing formula for markets with incomplete information and subjective views.
problem Asset pricing in markets with informational imperfections and subjective investor beliefs.
method Closed-form market equilibrium formula based on Merton's model, non-linear system of equations, conditional posterior distribution.
result Derivation of market reference model for excess returns under random shadow-costs.
This paper improves learning uncertain Bayesian networks from incomplete data.
problem Learning conditional probabilities in Bayesian networks with limited data.
method Develops methods to estimate and quantify uncertainty in conditional probabilities with incomplete data.
result Improves state-of-the-art approaches for handling uncertain Bayesian networks with incomplete data.
DynaCor detects noisy labels by learning from corrupted training signals.
problem Label noise in real-world datasets hinders model generalization.
method DynaCor introduces label corruption to indirectly simulate noisy labels and learns to distinguish clean from noisy instances.
result DynaCor outperforms state-of-the-art competitors in noisy label detection.
Paper proposes a tensor data model for incomplete imaging data.
problem Prognostics models for incomplete imaging data.
method Supervised tensor dimension reduction with TTF supervision and optimization.
result Model effectively extracts low-dimensional features from incomplete data.
New method for tensor classification with missing data.
problem Handling incomplete tensor data in high-dimensional classification.
method High-dimensional tensor linear discriminant analysis with TGMM and Tensor LDA-MD.
result Established convergence rates and minimax optimal bounds for misclassification rate.
Fairness audits fail under missing protected labels, especially at zero access.
problem Understanding the reliability of fairness audits with incomplete protected-label data.
method Introduced a seed-calibrated stress test to separate missingness effects from seed-to-seed movement.
result Missing protected labels do not significantly alter fairness mitigation methods, but they can lead to harmful intersectional outcomes.
PPI uses predictions to improve inference from incomplete data.
problem Incomplete or costly-to-measure outcomes in research fields.
method Leverages large unlabeled datasets for improved statistical efficiency with bias correction.
result PPI variants produce tighter confidence intervals than complete-case analysis.
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.
Let (M,g) be an open, oriented and incomplete riemannian manifold of dimension m. Under some general conditions we show that it is possible to build a Hilbert complex (L2Ωi(M,g),dM,i) such that its cohomology groups, labeled with H2,Mi(M,g), satisfy the following properties: \begi…
Hybrid model combines physics and data to handle incomplete systems.
problem Incomplete physics models with missing terms.
method Combines deep grey-box models with Optimal Transport.
result Enhances incomplete physics models with superior performance.
Semi-supervised EM improves convergence rate with labeled samples.
problem Improving convergence rate in EM algorithm with labeled and unlabeled data.
method Analysis of semi-supervised EM algorithm for Gaussian mixture models.
result Labeled samples significantly improve the convergence rate for the EM algorithm.
pRSL combines probabilistic rules to improve multi-label classification.
problem Modeling the structure between multi-label classes for better performance.
method Uses probabilistic propositional logic rules and belief propagation to combine predictions from multiple classifiers.
result pRSL achieves state-of-the-art performance on various benchmark datasets.
New method estimates model parameters from incomplete data.
problem Estimating model parameters from incomplete data.
method Variational Gibbs Inference (VGI)
result Competitive or better performance compared to existing methods.
A new method for state estimation in state-space models using incomplete data.
problem State estimation in nonlinear state-space models with incomplete observations.
method Statistical analysis of incomplete observations, score function, observed information matrices, EM-gradient-particle filtering.
result Maximum likelihood estimation of state-vector with explicit form of observed information matrix.
Machine learning models trained on indirect data labels can fail on real-world examples.
problem Validity issues in machine learning when target labels are indirectly defined.
method Identification of problematic datasets and models using a general procedure.
result Machine learning models trained on indirect data labels will fail on real-world examples.
GRAPE uses graph representation to handle missing data in feature imputation and label prediction.
problem Handling missing data in machine learning tasks.
method GRAPE uses a bipartite graph where observations and features are nodes, and observed feature values are edges. It formulates feature imputation as edge-level prediction and label prediction as node-level prediction, solving these with Graph Neural Networks.
result GRAPE achieves 20% lower mean absolute error for imputation and 10% lower for label prediction compared to state-of-the-art methods.
Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful. We propose a framework for training latent variable models that explicitly balances two goals: recovery of faithful generative explanations of high-dimensio…
The paper tackles learning true rankings from noisy, incomplete data.
problem Learning true rankings from incomplete and noisy data.
method Introduces a selective Mallows model for noisy rankings and derives upper and lower bounds on sample complexity.
result Strong asymptotically tight bounds on sample complexity for learning complete rankings and top-k rankings.
Paper uses deep learning to price and hedge options in incomplete markets.
problem Incomplete markets lack unique no-arbitrage solutions for pricing and hedging European options.
method Constrained deep learning approach with a single neural network representing option prices and hedging strategies.
result Constrained networks produce superior P&L distributions compared to unconstrained networks.
We construct genRBF kernel, which generalizes the classical Gaussian RBF kernel to the case of incomplete data. We model the uncertainty contained in missing attributes making use of data distribution and associate every point with a conditional probability density function. This allows to embed incomplete data i…
This work improves deep learning from noisy crowdsourced labels.
problem Learning label correction and neural classifier from noisy crowdsourced data.
method Coupled Cross-Entropy Minimization (CCEM) with identifiability and regularization.
result The CCEM criterion correctly identifies annotators' confusion and neural classifier under realistic conditions.
CoreFlow models matrix-valued distributions efficiently, preserving shared low-rank structure.
problem Learning matrix-valued distributions from high-dimensional and incomplete data.
method Low-rank flow model that learns shared row/column subspaces and trains a normalizing flow on the core.
result CoreFlow improves generation quality in few-sample regimes and remains competitive in data-rich settings.
This paper analyzes the equilibrium distribution of wealth in an economy where firms' productivities are subject to idiosyncratic shocks, returns on factors are determined in competitive markets, dynasties have linear consumption functions and government imposes taxes on capital and labour incomes and equally redistrib…
Paper studies how incomplete data affects machine learning, proposing 'Certain Predictions' for NN classifiers.
problem Impact of incomplete and inconsistent data on machine learning applications.
method Extends Certain Answers concept to machine learning, introduces 'Certain Predictions' for NN classifiers.
result Developed efficient solutions for CP queries in NN classifiers, improving classification accuracy.
PL-MCMC samples from normalizing flows' conditional distributions.
problem Sampling from complex conditional distributions learned by normalizing flows.
method Metropolis-Hastings implementation of PL-MCMC.
result PL-MCMC asymptotically samples from exact conditional distributions.
Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to…
Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks. However, typical GANs require fully-observed data during training. In this paper, we present a GAN-based framework for learning from comple…
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.
The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture models form the base models. Because of the Bayesian approach, TCK can naturally deal with missing values without re…
HSACC improves multi-view clustering of incomplete data.
problem Challenges in clustering incomplete multi-view data.
method Hierarchical Semantic Alignment and Cooperative Completion framework.
result HSACC outperforms state-of-the-art methods on benchmark datasets.