New research shows SSC fails when points on the same subspace are mislabeled.
problem Failure of SSC when points on the same subspace are mislabeled.
method Analyzed the effect of different distributions of points on the same subspace.
result SSC fails to infer correct labels when points on the same subspace fall into more than one cluster.
ELM detects mislabels in Finnish academic publication ranks.
problem Detecting mislabeled academic publication ranks in Finland.
method Used Extreme Learning Machine (ELM) with features characterizing publication channels.
result ELM-based approach accurately detected mislabels compared to reference results.
Paper identifies and removes mislabeled data to improve neural network training.
problem Improving neural network training by identifying and removing ambiguous or mislabeled data.
method Introduces Area Under the Margin (AUM) statistic to identify mislabeled data and a simple procedure to remove it.
result Consistently improves test error on synthetic and real-world datasets.
Study investigates how neural networks perform with mislabeled data.
problem Understanding and mitigating the effects of mislabeled training data on neural network performance.
method Analysis of model equations and use of Maximum Likelihood (ML) estimate to infer clean model parameters.
result ML estimate of noisy model parameters determines clean model parameters, leading to a classifier adjustment method.
Paper finds mislabeled instances in various datasets.
problem Mislabeled instances in labeled datasets.
method Non-parametric end-to-end pipeline for finding mislabeled instances.
result Average precision of more than 0.84 for finding top 1% mislabeled instances.
Improves neural network performance with double regularization.
problem Challenges of mislabeled data in neural networks.
method Double regularization combining model complexity and observation reweighting.
result Stronger robustness and generalization against mislabeling.
Efficient active learning method defends against malicious mislabeling and data poisoning attacks.
problem Malicious mislabeling and data poisoning attacks on deep neural networks.
method Adversarial retraining and active learning with random sampling strategy.
result The proposed method achieves 89% accuracy with only one-third of the labeled data, significantly outperforming the baseline method.
Improves fault detection models in noisy data.
problem Poor generalization due to mislabeled samples in fault detection.
method Two-step framework: outlier identification and data modification.
result Significantly improved model generalization under label noise.
Paper presents robust clustering methods for general mixture models.
problem Clustering with sub-Gaussian error assumptions often invalid in practice.
method Hybrid clustering with robust centroid estimate and data-driven initialization.
result Provably near-optimal mislabeling guarantees for general error distributions.
New attack tricks certifiably robust models into mislabeling images.
problem Defeating certified defenses against adversarial examples.
method Spoofed robustness certificates and large perturbations.
result Certifiably robust models can be fooled by large perturbations.
Do-AIQ framework evaluates AI algorithms' quality using DOE.
problem Quality evaluation of AI mislabel detection algorithms.
method Design-of-experiment approach with high-dimensional constraint space design and surrogate modeling.
result Established framework for evaluating AI algorithm quality robustly.
The paper proposes a method to detect and filter noisy or mislabeled data using pointwise mutual information.
problem Detecting and filtering noisy or mislabeled data in deep learning models.
method A mutual information-based framework quantifying statistical dependencies between inputs and labels.
result The method effectively filters low-quality samples, improving classification accuracy by up to 15%.
New method improves few-shot learning with noisy labels.
problem Robustness to label noise in few-shot learning.
method Feature aggregation and Transformer model for noisy samples.
result TraNFS outperforms other methods in noisy conditions.
The paper analyzes methods to identify influential data points in deep models.
problem Interpreting deep learning models and debugging datasets.
method Curated experiments to analyze influence of data points on classifiers.
result Training loss-based sample selection outperformed other methods in detecting mislabels.
Hölder-DPO aligns models robustly with noisy human feedback.
problem No existing alignment methods can handle severe label noise.
method Proposes Hölder-DPO, a principled alignment loss with provable redescending property.
result Hölder-DPO enables scalable human feedback valuation and improves model alignment.
DVGS identifies low-quality data quickly and accurately.
problem Identifying and filtering mislabeled or noisy data in machine learning.
method Data Valuation with Gradient Similarity (DVGS) algorithm.
result DVGS outperforms baseline methods in identifying low-value data across various domains.
Proposes a new loss function for better handling mislabeling costs.
problem Handling mislabeling costs in machine learning models.
method Introduces Real-World-Weight Crossentropy loss function.
result Demonstrates improved performance in scenarios of mislabeling.
RP method improves noisy label classification with CNN, achieving 0.46 error across all MNIST digits.
problem Noisy label binary classification with mislabeled examples.
method Rank Pruning (RP) for estimating noise rates and improving classification.
result RP achieves state-of-the-art performance in noisy label classification.
Dropout technique improves deep learning from noisy labels.
problem Training deep networks on datasets with unreliable labels.
method Augment deep network with a noise model and apply dropout regularization.
result The technique outperforms state-of-the-art methods on noisy datasets.
Efficiently creates label-consistent backdoor attacks without obvious mislabeling.
problem Vulnerability of deep neural networks to backdoor attacks that can be triggered by a backdoor trigger.
method Uses adversarial perturbations and generative models to inject inputs that are consistent with their labels.
result Demonstrates the feasibility of creating undetectable backdoor attacks by maintaining label-consistency.
Unified and noise-reduced data valuation framework for machine learning.
problem Quantifying the contribution of individual data points in machine learning.
method Beta Shapley, a generalization of Data Shapley, relaxes the efficiency axiom.
result Beta Shapley outperforms state-of-the-art data valuation methods on various ML tasks.
One-bit quantization and sparsification improve multiclass classification with strong regularization.
problem Overfitting mislabeled data in multiclass classification.
method Linear regression with regularization and one-bit quantization/sparsification.
result Sparse and one-bit solutions perform almost as well as the optimal solution with f(⋅)=∥⋅∥22. The study analyzes how deep neural networks treat instances with regular and irregular patterns.
problem Understanding how deep neural networks handle both common and rare patterns in data.
method Characterizes instances using a consistency score based on training data sets of varying sizes.
result The consistency score identifies out-of-distribution and mislabeled examples, distinguishing them from strongly regular examples.
New generalization concept considers distribution of errors, not just average error.
problem Classical generalization fails to capture distributional differences in classifier outputs.
method Formal conjectures about distributional generalization based on model architecture, training procedure, and data distribution.
result Distributional generalization can be expected in specific conditions, as evidenced by empirical results.
SAP corrects model for label noise by identifying and removing noisy samples.
problem Label corruption degrades model performance; acquiring perfect labels is costly.
method SAP uses SVD to identify and project model weights onto a clean activation space.
result SAP improves model generalization by up to 6% on CIFAR dataset with 25% synthetic corruption.
Adapts AUM to identify ambiguous tasks in crowdsourced learning, improving generalization.
problem Discerning ambiguous tasks in crowdsourced labels to prevent mislabeling.
method Introduces Weighted Areas Under the Margin (WAUM) to average AUMs weighted by task-specific scores.
result Improves generalization performance by discarding ambiguous tasks.
Study shows influence functions are poor for neural networks but useful for identifying influential examples.
problem Influence functions misalign with leave-one-out retraining in neural networks.
method Decomposed the discrepancy into five terms and studied their contributions across different architectures and datasets.
result Influence functions are a good approximation to the proximal Bregman response function (PBRF), useful for identifying influential examples.
Self-training algorithm improves classifier performance with labeled and unlabeled data.
problem Improving classifier performance with limited labeled data.
method Iterative learning of halfspaces, exploration and pruning phases.
result Misclassification error is bounded and never degrades compared to initial labeled set.
DataInf efficiently approximates data influence in large models, improving transparency and identifying mislabeled data.
problem Efficiently estimating data influence in large-scale models like LoRA-tuned LLMs and diffusion models.
method DataInf uses a closed-form expression to approximate influence scores efficiently.
result DataInf outperforms existing methods in computational and memory efficiency, accurately identifying influential data points.
Improved KNN data valuation method with reduced computation time.
problem Efficiently valuing individual data points in KNN models.
method Proposed a new utility function and derived its calculation for KNN classifiers/regressors, achieving similar time complexity as the original method.
result Soft-label KNN-SV outperforms the original method in mislabeled data detection.
Wavelet boosts gradient boosting, improving accuracy, especially in imbalanced data.
problem Improving gradient boosting performance, especially in imbalanced data.
method Wavelet decomposition of trees in gradient boosting.
result Wavelet-based gradient boosting outperforms existing methods, especially in imbalanced data.
Develops new methods to evaluate data influence in SAM for improved model training.
problem Challenges in mislabeled noisy data and privacy concerns in SAM.
method Two innovative data valuation methods based on influence functions (IF) for SAM.
result Demonstrates effectiveness in identifying mislabeled data and enhancing interpretability.
Simple k-NN filtering improves model accuracy on noisy labels.
problem Training models with noisy labels reduces performance and is hard to identify.
method A simple k-nearest neighbor-based filtering approach on the logit layer. result Improves model accuracy compared to recent methods.
PUMA augments models to remove unique data points without performance loss.
problem Preserving model performance while removing unique training data points.
method Explicitly models data influence, reweights remaining data optimally.
result PUMA effectively removes unique data points without performance degradation.
EP algorithm for efficient feature selection in binary classification.
problem Sparse feature selection in binary classification.
method Statistical mechanics inspired expectation propagation (EP) on a diluted Bayesian classifier.
result EP is a robust and competitive algorithm in terms of variable selection, estimation accuracy, and computational complexity.
Boosting is known to be sensitive to label noise. We studied two approaches to improve AdaBoost's robustness against labelling errors. One is to employ a label-noise robust classifier as a base learner, while the other is to modify the AdaBoost algorithm to be more robust. Empirical evaluation shows that a committee of…
FASC clusters data with latent factors, improving on naive methods.
problem Clustering high-dimensional data with correlated variables.
method Factor Adjusted Spectral Clustering (FASC) algorithm.
result FASC achieves an exponentially low mislabeling rate under general assumptions.
New model handles noisy labels in semi-supervised classification.
problem Noisy class labels in classification tasks.
method M-VAE: A semi-supervised deep generative model that explicitly models noisy labels.
result M-VAE performs better than models ignoring label noise.
Regularized linear regression improves binary classification performance, especially with ridge and ℓ1 regularization.
problem Improving binary classification accuracy with noisy labels.
method Systematic study of regularization strengths on linear classifiers trained on noisy binary classification data.
result Ridge regression consistently improves classification error, while ℓ1 regularization can induce sparsity and ℓ∞ regularization can concentrate weights to two values. Annotation errors can significantly hurt classifier performance, yet datasets are only growing noisier with the increased use of Amazon Mechanical Turk and techniques like distant supervision that automatically generate labels. In this paper, we present a robust extension of logistic regression that incorporates the po…
Paper uses random projection to preserve subspace structure for efficient data analysis.
problem Efficiently analyzing data with low-dimensional structure.
method Compressed Subspace Learning (CSL) framework based on Johnson-Lindenstrauss property.
result Random projection preserves the UoS structure of data, enabling efficient analysis.
This paper covers robust subspace learning and tracking methods.
problem Learning and tracking subspaces in the presence of outliers.
method Robust PCA, Robust Subspace Tracking, Robust Subspace Recovery.
result Effective methods for handling outliers in subspace learning and tracking.
Detects missing tensor signals in a KS subspace with high probability.
problem Detecting tensor signals with many missing entities in a KS subspace.
method Projecting the signal onto the KS subspace and bounding residual energy.
result Reliable detection is possible if the missing signal cardinality exceeds KS subspace dimensions.
Study reveals pervasive label errors in test sets, affecting machine learning benchmarks.
problem Label errors in test sets destabilize machine learning benchmarks.
method Identified label errors in 10 common datasets using confident learning algorithms and human validation.
result Lower capacity models may be more useful in real-world datasets with high proportions of erroneously labeled data.
Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling outliers and mislabeled instances is to remove any instance that is misclassified by a learning algorithm. However, an examination of which l…
Paper bounds subspace estimator error from noisy projections.
problem Estimating subspaces from noisy data.
method Derives perturbation bound on optimal subspace estimator.
result Fundamental result with implications in matrix completion and clustering.
Paper explores tradeoffs in classification using tensor subspaces.
problem Supervised classification with sample, computation, and storage complexities.
method Use of tensor subspaces, particularly hierarchical Kronecker structured subspaces.
result Hierarchical Kronecker structured subspaces improve classification tradeoffs.
In subspace clustering, a group of data points belonging to a union of subspaces are assigned membership to their respective subspaces. This paper presents a new approach dubbed Innovation Pursuit (iPursuit) to the problem of subspace clustering using a new geometrical idea whereby subspaces are identified based on the…