New robust PCA method minimizes trimmed reconstruction error over Stiefel manifold.
problem Outliers affect PCA's accuracy; robustification needed.
method Directly minimizes trimmed reconstruction error over the Stiefel manifold without deflation.
result Outperforms or matches state-of-the-art methods in efficiency and accuracy.
Paper presents a robust estimator for density ratio estimation that trims outliers.
problem Vulnerability of density ratio estimation to corrupted data points.
method Automatically identifies and trims outliers in density ratio estimation; uses convex formulation and subgradient descent.
result Global optimum can be obtained via subgradient descent; parameter estimation error analyzed under high-dimensional settings.
A method for estimating parameters from entangled single-sample distributions, robust to high-noise data.
problem Estimating common parameters from entangled single-sample distributions.
method Iterative trimming of samples to estimate the parameter.
result The method can tolerate a constant fraction of high-noise data points.
Alpha-trimming prunes trees in random forests to improve predictive performance.
problem Improving predictive performance of random forests by locally adaptive tree pruning.
method Alpha-trimming is a fast pruning algorithm that prunes trees in a random forest based on signal-to-noise ratio, controlled by a tuning parameter.
result Alpha-trimming often lowers mean squared prediction error compared to fully grown random forests.
Proposes a robust elastic net model for high-dimensional data.
problem High-dimensional sparse regression with outliers.
method Robust Elastic Net (REN) model with trimmed inner product.
result Performance guarantees for statistical and optimization errors.
Improved robust regression for heavy-tailed and contaminated data.
problem Linear regression with heavy-tailed and adversarially contaminated covariates and responses.
method Applying a filtering algorithm to covariates and then using Huber regression, least trimmed squares, or least absolute deviation estimators on the remaining data.
result Near-optimal error rates achieved for the Huber regression estimator.
Paper proposes iterative trimmed loss minimization for learning from corrupted data.
problem Learning from corrupted training data.
method Iterative trimmed loss minimization, alternating between selecting and retraining samples.
result Recovery of ground truth with linear convergence rate in generalized linear models.
Robust estimators improve high-dimensional data analysis by trimming outliers.
problem Robustifying high-dimensional structured estimation in the presence of outliers and data corruption.
method Trimmed versions of structurally regularized M-estimators, including Least Trimmed Squares and Lasso, are analyzed and optimized.
result General analysis and guarantees for statistical convergence and consistency of trimmed estimators.
New algorithm reduces ERM problem size while maintaining accuracy.
problem Empirical risk minimization problem size reduction.
method Adaptive Deterministic Uniform-Weight Trimming (ADUWT) algorithm.
result Uniform (1±ε) relative-error approximation for ERM objective. Trimmed Lasso offers sparse modeling with robustness control.
problem Sparse modeling in linear regression with robustness.
method Trimmed Lasso penalty function and its analysis.
result Trimmed Lasso offers exact sparsity control and robustness.
New method measures model variability from stochastic optimization.
problem Measuring model quality obscured by stochastic optimization.
method Robust hypothesis testing and novel summary statistic.
result Shows α-trimming level is more expressive than performance metrics. Study connects geodesic sums to area constant.
problem Understanding geodesic sums in Teichmüller space.
method Relates trimmed sums of twists to area Siegel-Veech constant.
result Established connection between geodesic sums and area constant.
Trimming helps in conformal prediction when it separates anomaly scores.
problem Effectiveness of trimming in conformal prediction under contamination.
method Analyse fixed-threshold trimming as a replacement of the contaminated calibration law with a retained law.
result Trimming helps when it separates anomaly scores, reducing clean-target coverage to a one-dimensional score-CDF transfer problem.
Paper develops robust distributed learning algorithms against Byzantine failures.
problem Security issues in large-scale distributed learning, especially Byzantine failures.
method Developed robust distributed gradient descent algorithms using median and trimmed mean operations.
result Achieved order-optimal statistical error rates for strongly convex losses.
New method trims network data to resist adversarial contamination.
problem Adversarial contamination in network data affects statistical and algorithmic performance.
method Proposes a new trimming method operating in model space to address both block and white noise contamination.
result Demonstrates superior performance in simulations compared to direct trimming.
The paper proposes a method to trim Bayesian network classifiers robustly.
problem Removing costly features from Bayesian network classifiers while maintaining robustness.
method Introduces an expected classification agreement (ECA) metric and a branch-and-bound search algorithm to find optimal feature subsets and thresholds.
result The proposed method maximizes expected agreement between the original and trimmed classifiers, subject to a budgetary constraint.
We describe a general framework for measuring risks, where the risk measure takes values in an abstract cone. It is shown that this approach naturally includes the classical risk measures and set-valued risk measures and yields a natural definition of vector-valued risk measures. Several main constructions of risk meas…
Paper develops robust OPF method using contextual information.
problem Optimal Power Flow problem under incomplete uncertainty knowledge.
method Distributionally robust chance-constrained formulation with probability trimmings and optimal transport.
result Distributional robustness improves expected cost and system reliability.
New method solves sparse approximation problem using trimmed lasso and generalized soft-min penalties.
problem Sparse approximation or best subset selection problem.
method Regularized approach with trimmed lasso and generalized soft-min penalties.
result The trimmed lasso provides sparse recovery guarantees and a practical optimization algorithm.
New method guards against outliers in sparse GGMs estimation.
problem Outliers in high-dimensional data affect robust estimation of GGMs.
method Trimmed Graphical Lasso for robust sparse GGMs estimation.
result Our method provides statistical guarantees and outperforms existing approaches.
Net-Trim prunes deep neural networks to reduce connections while maintaining accuracy.
problem Redundancy and overfitting in deep neural networks reduce model performance.
method Layer-wise pruning using convex optimization to remove connections at each layer.
result Net-Trim reduces the number of connections in neural networks without sacrificing accuracy.
This study analyzes LTS in sparse models with finite sample error bounds.
problem Robust regression in high-dimensional sparse models with limited data.
method Non-asymptotic analysis of LTS error bounds.
result Established finite sample error bounds for LTS in sparse models.
New core inflation measure predicts future headline inflation.
problem Creating a better measure of core inflation for timely policy decisions.
method Assemblage Regression, a nonnegative ridge regression that optimizes subcomponent weights.
result Significant improvements in forecasting medium-term inflation developments.
New method prevents neural network breakdown by combining trimmed loss and variation regularization.
problem Outlier contamination in neural network training.
method Integrates transformed trimmed loss and higher-order variation regularization.
result Ensures robustness to outlier contamination with a high functional breakdown point.
Paper proposes robust gossip algorithms for mean and trimmed mean estimation.
problem Vulnerability of mean-based gossip algorithms to malicious nodes.
method Developed extsc{GoRank} for rank estimation and extsc{GoTrim} for trimmed mean estimation.
result Established convergence rates for rank and trimmed mean estimation.
TrIM improves gradient-based dimension reduction and regression.
problem Efficiently identifying relevant feature subspace for high-dimensional regression.
method Introduced TrIM forest, an iterative approach using Mondrian forest and EGOP estimate.
result Consistency guarantees and convergence rates for EGOP matrix and random forest estimator.
New algorithm robustly estimates sparse models in high dimensions with corrupted data.
problem Estimating latent variable models with arbitrarily corrupted samples in high dimensional space.
method Trimmed (Gradient) Expectation Maximization with trimming gradients and hard thresholding steps.
result The algorithm converges to near optimal statistical rate geometrically under certain conditions.
Estimates GLMs robustly against label corruptions.
problem Learning GLMs under adversarial label corruptions.
method Iterative trimmed maximum likelihood estimator.
result Achieves minimax near-optimal risk.
Net-Trim simplifies neural networks by pruning layers efficiently.
problem Simplifying trained deep neural networks for efficiency.
method Net-Trim is a convex post-processing technique that prunes neural network layers.
result Net-Trim can find a network with a limited number of nonzero terms from a small number of samples.
iTimER learns from reconstruction errors to represent irregularly sampled time series.
problem Learning from irregularly sampled time series with missing data.
method iTimER models reconstruction errors as a proxy for unobserved values, using a mixup strategy and a Wasserstein metric.
result iTimER outperforms state-of-the-art methods in classification, interpolation, and forecasting tasks.
Method calculates Shapley values for PCA reconstruction errors to explain anomaly detection.
problem Explaining PCA-based anomaly detection results.
method Utilizes probabilistic PCA view to compute Shapley values of reconstruction errors.
result Shapley values are more advantageous than raw errors for explaining anomalies.
A new robust GP regression algorithm that trims outliers improves model accuracy.
problem Severe bias in GP regression due to data contamination by outliers.
method Iterative trimming of extreme data points.
result Significantly outperforms standard and robust GP variants in most test cases.
Paper proposes RAN for better anomaly detection in time series data.
problem Anomaly detection algorithms often fail to accurately detect anomalies due to incomplete reconstruction of anomaly data.
method RAN uses adversarial learning and latent vector-constrained Autoencoder to ensure consistent reconstruction of anomaly data.
result RAN outperforms other algorithms in detecting meaningful anomalies with higher AUC-ROC scores.
TRIM improves interpretability of deep neural networks in cosmology.
problem Understanding which features a deep neural network uses in a transformed space.
method TRIM (Transformation IMportance) attributes importances to features in a transformed space.
result Combining TRIM with contextual decomposition helps identify physical features learned by DNNs.
CoRAS adapts image acquisition rates for accurate reconstruction.
problem Determining when enough measurements are collected for accurate image reconstruction.
method Adaptive acquisition rate selection based on reconstruction error probability.
result CoRAS achieves target stopping-time coverage with fewer measurements.
Paper improves MRI reconstruction by separating target labels and prediction error.
problem Improving MRI reconstruction accuracy by estimating prediction error.
method Proposes a novel method to estimate target labels and prediction error separately.
result Significantly better MRI reconstruction results achieved compared to state-of-the-art methods.
We assess cluster stability by trimming extreme points and tracking data range reduction.
problem Assessing stability of one-dimensional clusters.
method Probabilistic method using diameter-shrinkage ratio to track data range reduction.
result Our method achieves higher accuracy than classical tests in small or noisy samples.
A robust clustering method for noisy data using Bregman divergences.
problem Clustering data corrupted with clutter noise.
method k-means type method based on Bregman divergences with a trimming approach.
result Empirically optimal codebook converges to an optimal codebook in the distortion sense.
A new algorithm identifies outliers in Gaussian clustering models.
problem Handling outliers in Gaussian model-based clustering.
method OCLUST algorithm removes least plausible points based on subset log-likelihoods until they adhere to a reference distribution.
result OCLUST inherently estimates the number of outliers.
New method reconstructs networks with unknown and varying errors.
problem Network datasets often contain errors and omissions, limiting traditional analysis.
method Bayesian reconstruction approach that handles heterogeneous errors and single edge measurements.
result Efficient nonparametric inference for hierarchical community structure from noisy data.
Probabilistic Autoencoder learns latent space weights' distribution.
problem Nonlinear model reconstruction error and sample quality.
method Normalizing flow for latent space weights' probability distribution.
result PAE achieves small reconstruction errors, high sample quality, and good performance.
DCAE learns compact latent representations for one-class novelty detection.
problem Learning compact latent representations for one-class novelty detection.
method DCAE learns compact and collapse-free latent representations through internal discriminative layers of GANs, reconstructing in-class data finely and exclusively.
result DCAE achieves state-of-the-art performance on novelty and adversarial example detection.
Sparse elasticity reconstruction from local displacements reduces error.
problem Reconstructing elasticity from limited data.
method Sparse elasticity reconstruction theory, local clustering, alternating optimization.
result Higher spatial resolution elasticity distribution estimation.
We propose a general framework for reconstructing and denoising single entries of incomplete and noisy entries. We describe: effective algorithms for deciding if and entry can be reconstructed and, if so, for reconstructing and denoising it; and a priori bounds on the error of each entry, individually. In the noiseless…
GCVAE improves disentanglement in VAEs while balancing reconstruction error.
problem Improving disentanglement in VAEs while maintaining low reconstruction error.
method Introduces three controllable Lagrangian hyperparameters to optimize reconstruction and KL divergence loss.
result GCVAE outperforms state-of-the-art models in disentanglement while balancing reconstruction.
RECol generates error columns to improve outlier detection.
problem Outlier detection in data with complex relationships.
method Generates reconstruction error columns for leave-one-out feature sets.
result Improves ROC-AUC and PR-AUC values of common outlier detection methods.
Paper proposes a framework to identify and obfuscate sensitive features via information density estimation.
problem Identifying and protecting sensitive attributes from leakage in obfuscation mechanisms.
method Information density estimation to identify leaking features, followed by a targeted obfuscation mechanism.
result Proven leakage guarantee in terms of Eγ-divergence for the obfuscation mechanism. New algorithm learns stable LDSs with lower error and better control performance.
problem Learning stable LDSs from data with minimal reconstruction error and stability constraints.
method Proposes an optimization method using a recent characterization of stable matrices, iteratively improving reconstruction error and ensuring stability.
result Achieves orders-of-magnitude improvement in reconstruction error compared to existing methods.