Bayesian method estimates contamination factor for unsupervised anomaly detection.
problem No good methods for estimating contamination factor in unsupervised anomaly detection.
method Bayesian approach using mixture formulation of anomaly detector outputs.
result Estimated contamination factor distribution is well-calibrated and improves anomaly detection performance.
The paper analyzes how conformal prediction works with contaminated reference data.
problem The impact of contamination on the validity and power of conformal prediction methods.
method The paper analyzes the impact of contamination on the validity of conformal methods and proposes a data-cleaning framework to enhance power.
result The proposed data-cleaning framework can effectively enhance power while maintaining type-I error control.
A contaminated mixture model detects outliers in multivariate functional data.
problem Detecting abnormal sensor measurements in multivariate functional data.
method A contaminated mixture model that clusters and detects outliers without specifying outlier proportion.
result The model outperforms competitors and correctly detects abnormal behaviors in real data.
Improves anomaly detection with contaminated unlabeled data.
problem Weakness in existing semi-supervised anomaly detection methods when unlabeled data contain anomalies.
method Integrates positive-unlabeled learning with deep anomaly detection models.
result Achieves better detection performance on various datasets.
New test ensures quality of shared data in machine learning.
problem Ensuring quality of external data in machine learning tasks.
method Distribution-free two-sample testing procedures grounded in conformal outlier detection.
result Identifies valuable external data agents for model personalization.
In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely. Among activities that impact the environment, oil and gas production, …
A robust loss for anomaly mitigation and unsupervised contamination classification
problem Detecting and mitigating contamination in supervised and unsupervised settings
method Neural Bayesian Anomaly Mitigation (NBAM)
result Recovering the structure of contamination and identifying label-flip pairs
Study tackles contamination and heterogeneity in multi-task learning, improving robustness and personalization.
problem Challenges in integrating related tasks due to contamination and heterogeneity.
method Proposes a filtering-based robust multi-task gradient descent method to estimate global and clean task-specific minimizers.
result Demonstrates improved robustness and personalization compared to existing methods.
A new method detects distribution shifts faster than existing CTMs.
problem Detecting distribution shifts in data streams with contamination issues.
method Uses a fixed reference dataset to compare each new sample, avoiding contamination.
result Detects distribution shifts faster and more reliably than standard CTMs.
Paper proposes CLAIR for efficient LLM fine-tuning across clients.
problem Fine-tuning large language models (LLMs) efficiently and collaboratively.
method Federated LoRA fine-tuning with Collaborative Low-rank Alignment and Identifiable Recovery (CLAIR).
result CLAIR achieves better performance and contamination detection compared to local fine-tuning.
According to the United Nations World Water Assessment Programme, every day, 2 million tons of sewage and industrial and agricultural waste are discharged into the worlds water. In order to address this pervasive issue of increasing water pollution, while ensuring that the global population has an efficient, accurate, …
VSCOUT detects anomalies in high-dimensional data using a hybrid VAE approach.
problem Challenges in classical SPC for high-dimensional, non-Gaussian data.
method Hybrid VAE architecture with ARD prior, ensemble filtering, and changepoint detection.
result VSCOUT achieves superior sensitivity to special-cause structure and controlled false alarms.
ABIForest improves anomaly detection using attention weights.
problem Anomaly detection in datasets.
method Attention mechanism integrated into Isolation Forest.
result ABIForest outperforms standard Isolation Forest on synthetic and real datasets.
Mixtures of multivariate contaminated shifted asymmetric Laplace distributions are developed for handling asymmetric clusters in the presence of outliers (also referred to as bad points herein). In addition to the parameters of the related non-contaminated mixture, for each (asymmetric) cluster, our model has one param…
Ano-SuPs detects anomalies in images of manufactured products by identifying suspected patches.
problem Challenges in detecting anomalies in image-based manufacturing systems, including complexity of background and various anomaly patterns.
method Two-stage strategy anomaly detection method: first, remove suspected patches; second, refine anomaly identification using normal patches.
result Demonstrated effectiveness through simulation and case studies, identifying key parameters and steps impacting model performance and efficiency.
Robust anomaly detection is a requirement for monitoring complex modern systems with applications such as cyber-security, fraud prevention, and maintenance. These systems generate multiple correlated time series that are highly seasonal and noisy. This paper presents a novel unsupervised deep learning architecture for …
Study improves robustness of Bayesian inference for cognitive models.
problem Outliers and contaminants affect parameter estimation in cognitive models.
method Robustness of parameter estimation using amortized Bayesian inference (ABI) with neural networks.
result Introducing contaminants from a Cauchy distribution increases robustness.
Study finds no significant alignment between whitepaper claims and market structure.
problem Correlation between cryptocurrency whitepaper narratives and market behavior.
method Developed a contamination-aware pipeline for measuring structural correspondence, combining NLP classification and market statistics.
result No significant claims-market alignment detected in the sample.
New model detects anomalies robustly in noisy, seasonal multivariate time series.
problem Detecting anomalies in noisy, seasonal multivariate time series data.
method Proposes Robust Seasonal Multivariate Generative Adversarial Network (RSM-GAN).
result Improves robustness and precision in detecting anomalies.
Detects corruption in agentic models during execution.
problem Inconsistent context, retrieval errors, or adversarial inputs corrupt intermediate steps of reasoning chains.
method Analyzes token graphs induced by attention and computes spectral statistics to emit accept/reject signals.
result A single threshold on the high frequency energy ratio optimally detects context inconsistency in agentic models.
Existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data. However, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. In this paper, we propose a…
Paper explores embedding methods for detecting pseudo-cliques in random graphs, showing limitations and potential.
problem Detecting planted pseudo-cliques in random dot product graphs.
method Adjacency Spectral Embedding (ASE) and Graph Encoder Embedding (GEE).
result These methods can localize pseudo-cliques with additional clean network data, but not without it.
Enhances network intrusion detection in noisy data.
problem Robustness against contaminated and noisy data inputs in network intrusion detection.
method Probabilistic Temporal Graph Network Support Vector Data Description (TGN-SVDD) model.
result Significant improvements in detection performance with synthetic noise.
RKUM is an R package for robust kernel-based unsupervised methods.
problem Robust analysis under contaminated or noisy data conditions.
method Robust kernel covariance and cross-covariance operators using generalized loss functions.
result RKUM reduces sensitivity to contamination and effectively identifies outliers.
FCI method uses flow-based techniques to improve prediction confidence.
problem Limited applicability of exchangeable assumptions in predicting contaminated data.
method Adversarial flow to transform data into known distributions, then map to low-dimensional space.
result FCI produces effective predictive sets and accurate outlier detection.
Robust fuzzy clustering for EEG driver alertness with outlier detection.
problem Ambiguous state boundaries in multivariate time series data.
method RFCPCA, a robust fuzzy subspace-clustering method for MTS.
result RFCPCA improves clustering accuracy and characterizes uncertainty and outliers in MTS.
Study improves MMD estimation for two distributions with mismeasured data.
problem Estimating MMD under contaminated data.
method Partial identification of MMD bounds with a fast convergence rate.
result Proposes a method to estimate MMD bounds that converge faster than alternatives.
A contamination in a 3-manifold is an object interpolating between the contact structure and the lamination. Contaminations seem to provide a link between 3-dimensional contact geometry and the classical topology of 3-manifolds, as described in a separate paper. In this paper we deal with contaminations carried by bran…
Study minimax robustness in statistical estimation under Wasserstein contamination.
problem Adversarial perturbations in statistical data.
method Developed minimax theory for ℓqr losses under Wasserstein-r contaminations. result Exact minimax risk identified for joint contaminations in location estimation and prediction in linear regression.
In genome-wide interaction studies, to detect gene-gene interactions, most methods are divided into two folds: single nucleotide polymorphisms (SNP) based and gene-based methods. Basically, the methods based on the gene are more effective than the methods based on a single SNP. Recent years, while the kernel canonical …
Study shows robust method for estimating density ratios even with heavy contamination.
problem Estimating density ratios in the presence of heavy contamination.
method Weighted density ratio estimation (DRE) with doubly strong robustness.
result Weighted DRE achieves sparse consistency under heavy contamination.
Generative models can still learn from contaminated data, but with limitations.
problem How much contamination can generative models tolerate?
method Characterized robustness under contaminated enumerations, proving generation is achievable for all countable collections if contamination fraction converges to zero.
result Generation under contamination is achievable for all countable collections if contamination fraction converges to zero, but dense generation is strictly less robust.
Algorithm identifies Pareto set in bandits with contaminated feedback.
problem Identifying Pareto set in multi-objective bandits with adversarial contamination.
method Sample median-based multi-objective adaptive elimination algorithm.
result Sample complexity bound that depends on contamination probability.
To the best of our knowledge, there are no general well-founded robust methods for statistical unsupervised learning. Most of the unsupervised methods explicitly or implicitly depend on the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). They are sensitive to contaminated data, …
Deep learning model detects and corrects outliers in crowd-sourced weather data.
problem Data quality issues in crowd-sourced weather data.
method Bayesian deep learning approach with Gaussian-uniform mixture density network.
result Automated outlier detection in spatio-temporal environmental modeling.
New algorithm resists contamination in high-dimensional regression with optimal performance.
problem Adversarial and measurement errors in high-dimensional data.
method Adversarial Contamination-resistant Iterative Hard Thresholding (AC-IHT) algorithm.
result Achieves minimax near-optimal estimation and signal-adaptive support recovery.
Open category detection is the problem of detecting "alien" test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms …
A new method optimizes anomaly scoring from score distribution to improve AD performance.
problem Vulnerability to anomaly contamination and lack of adaptability in existing AD methods.
method Optimizes anomaly scoring function from score distribution perspective, using Overlap loss.
result Overlap loss-based AD models significantly outperform state-of-the-art methods.
Nowadays, how to effectively evaluate visual properties has become a popular topic for fine-grained visual comprehension. In this paper we study the problem of how to estimate such visual properties from a ranking perspective with the help of the annotators from online crowdsourcing platforms. The main challenges of ou…
Motivated by applications of bandit algorithms in education, we consider a stochastic multi-armed bandit problem with ε-contaminated rewards. We allow an adversary to give arbitrary unbounded contaminated rewards with full knowledge of the past and future. We impose the constraint that for each time t the…
Paper introduces RAS for robust MTL with contamination.
problem Representation-based multi-task learning struggles with contamination.
method Robust and Adaptive Spectral (RAS) method.
result RAS prevents negative transfer and performs well with up to 80% contamination.
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.
Improved robust regression with clean covariates achieves better rates than Huber's model.
problem Robust regression under adaptive contamination of responses with clean covariates.
method Exploiting clean covariates to construct an estimator achieving better rates than Huber's model.
result Improved estimation rate even with constant contamination, achieving consistency.
Many computer vision tasks involve processing large amounts of data contaminated by outliers, which need to be detected and rejected. While outlier detection methods based on robust statistics have existed for decades, only recently have methods based on sparse and low-rank representation been developed along with guar…
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.
New algorithm reduces contamination in supervised learning.
problem Learning with contamination in supervised learning.
method Iterative polynomial filtering.
result Efficient learning of functions with contamination.
Paper shows MoM is optimal under adversarial contamination for certain distributions.
problem Optimality of MoM under adversarial contamination.
method Upper and lower bounds for MoM's error under adversarial contamination.
result MoM is (minimax) optimal for distributions with finite variance and infinite variance with finite absolute moments.
Paper tackles outlier detection in signals modeled by generative models with theoretical guarantees.
problem Recovering signals from linear measurements with sparse outliers.
method Proposes an iterative ADMM algorithm and gradient descent algorithm for outlier detection using ℓ1 and squared ℓ1 norm minimization. result Establishes theoretical recovery guarantees for signal reconstruction under sparse outliers.