Robust TOT regression method handles outliers in tensor data.
problem Outliers in tensor data affect standard TOT regression.
method ROTOT method using a single loss function for outliers and robust MPCA for predictor.
result ROTOT method reduces influence of both casewise and cellwise outliers.
Outliers in weights and activations pose a key challenge for fixed-point quantization of neural networks. While they can be addressed by fine-tuning, this is not practical for ML service providers (e.g., Google or Microsoft) who often receive customer models without training data. Specialized hardware for handling acti…
Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attribute…
New robust MPCA method handles casewise and cellwise outliers in tensor data.
problem Outliers, especially casewise and cellwise, affect the performance of standard MPCA.
method Uses a single loss function to reduce the influence of both types of outliers and missing values.
result The new method improves robustness and performance in tensor data analysis.
Clustering, or unsupervised classification, is a task often plagued by outliers. Yet there is a paucity of work on handling outliers in clustering. Outlier identification algorithms tend to fall into three broad categories: outlier inclusion, outlier trimming, and post hoc outlier identification methods, with the forme…
In this paper, a scale mixture of Normal distributions model is developed for classification and clustering of data having outliers and missing values. The classification method, based on a mixture model, focuses on the introduction of latent variables that gives us the possibility to handle sensitivity of model to out…
Cluster analysis and outlier detection are strongly coupled tasks in data mining area. Cluster structure can be easily destroyed by few outliers; on the contrary, outliers are defined by the concept of cluster, which are recognized as the points belonging to none of the clusters. Unfortunately, most existing studies do…
A robust model handles up to 25% of outliers in time-series data for power flow calculations.
problem Handling outliers in time-series data for accurate power flow calculations.
method Robust data-driven process model with Schweppe-type generalized maximum likelihood estimator and projection statistics for outlier weighting.
result The model can handle up to 25% of outliers in the training data set.
ORAT improves model robustness against outliers and adversarial attacks.
problem Challenges of training data like outliers and adversarial samples.
method Bi-level optimization with robust rank-based loss function.
result ORAT achieves theoretical consistency and uniform convergence rates.
Robust PCA, the problem of PCA in the presence of outliers has been extensively investigated in the last few years. Here we focus on Robust PCA in the outlier model where each column of the data matrix is either an inlier or an outlier. Most of the existing methods for this model assumes either the knowledge of the dim…
Proposes CE-BASS for robust Kalman filtering with innovative and additive outliers.
problem Robustness to both innovative and additive outliers in Kalman filtering.
method Particle mixture Kalman filter with re-sampling of past states.
result CE-BASS efficiently handles multi-modality and trend changes in hidden state distributions.
Paper develops a method to robustly cluster tensors with outliers.
problem Clustering tensors contaminated by outliers or sample-specific corruptions.
method Transformed Tensor Low-Rank Representation (OR-TLRR) method.
result Provably recovers row space of clean data and detects outliers.
Adaptive methods learn from multiple datasets, leveraging similarities and robust to outliers.
problem Simultaneously analyze multiple datasets with possible similarities and differences.
method Adaptive multi-task learning methods that automatically utilize similarities and handle differences.
result Sharp statistical guarantees and robustness against outlier tasks demonstrated.
A new robust regression method handles outliers in high-dimensional data.
problem Outliers in high-dimensional data make conventional regression methods ineffective.
method Robust penalized least squares of depth trimmed residuals regression.
result The new method outperforms existing methods in estimation and prediction accuracy.
Notwithstanding the popularity of conventional clustering algorithms such as K-means and probabilistic clustering, their clustering results are sensitive to the presence of outliers in the data. Even a few outliers can compromise the ability of these algorithms to identify meaningful hidden structures rendering their o…
A new KF handles outliers without MSE loss.
problem Outliers degrade Kalman filter performance.
method NUV priors, EM and AM for variance estimation.
result Outlier-insensitive KF outperforms existing methods.
We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning. Our proposed method handles both high-dimension low-sample size and traditional low-dimensional high-sample size datasets. Essentially, we avoid the computational bottleneck of techniques like minimum covaria…
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…
Improves data normality with robust transformations.
problem Skewed data distribution.
method Modified Box-Cox and Yeo-Johnson transformations with robust parameter estimation.
result Transformed data approximates normality in the center with outliers.
Paper proposes DMGD for integrating outlier and community detection in graph embedding.
problem Outlier nodes affect graph embedding of regular nodes, especially in networks with multiple communities.
method DMGD integrates outlier and community detection with node embedding using multiclass graph description.
result DMGD detects outliers relative to their communities and achieves better node embedding compared to state-of-the-arts.
Paper tackles event stream outliers using a novel weight function.
problem Learning event streams with unexpected absences or occurrences.
method Temporal point process framework with a novel weight function.
result The proposed method effectively handles both commission and omission outliers.
We develop efficient algorithms for robust PCA that handle outliers.
problem Finding principal components in datasets with outliers.
method Nearly-linear time and streaming algorithms for robust PCA.
result Near-optimal error guarantees for robust PCA with nearly-linear time and memory usage.
New PCA method handles multiple datasets and detects sparse patterns robustly.
problem Handling multi-source data with sparse and outlier-robust PCA.
method Developed a regularization problem with a penalty for structured sparsity and outlier resistance.
result The method detects global and local patterns across multiple data sources robustly.
Random Forests are reinterpreted as generative models to handle missing data and detect outliers.
problem Handling missing features and detecting outliers in Random Forests.
method Interpreting Random Forests as Probabilistic Circuits and applying marginalisation for missing data.
result GeDTs and GeFs can handle missing data and detect outliers under certain assumptions.
Spatial perception is the backbone of many robotics applications, and spans a broad range of research problems, including localization and mapping, point cloud alignment, and relative pose estimation from camera images. Robust spatial perception is jeopardized by the presence of incorrect data association, and in gener…
We describe our methods that achieved the 3rd and 4th places in tasks 1 and 2, respectively, at ISIC challenge 2019. The goal of this challenge is to provide the diagnostic for skin cancer using images and meta-data. There are nine classes in the dataset, nonetheless, one of them is an outlier and is not present on it.…
New algorithms detect outliers in high-dimensional data with arbitrary shapes.
problem Challenges of high dimensionality and varying cluster shapes in traditional outlier detection methods.
method Cluster Catch Digraphs (CCDs) and their variants (U-MCCD, UN-MCCD, SU-MCCD, SUN-MCCD).
result U-MCCD efficiently identifies outliers with high true negative rates, and SU-MCCD improves handling of non-uniform clusters.
Scalable and robust TR decomposition for large-scale data with missing entries and outliers.
problem Handling large-scale tensor data with missing entries and outliers.
method Auto-weighted steepest descent method for missing entries and outliers identification, FGMC and RStS strategies.
result Outperforms existing TR decomposition methods in the presence of outliers and runs faster than robust tensor completion algorithms.
Proposes GMOTE for better handling imbalanced data.
problem Classification of imbalanced data.
method Generates minority class instances using Gaussian Mixture Model, considering outliers through Mahalanobis distance.
result Shows better accuracy and F1-Score when combined with CART or SVM.
A new robust Wasserstein distance is proposed to handle outliers in probability distributions.
problem Outliers in probability distributions make Wasserstein distances sensitive and impractical.
method Introduces a new outlier-robust Wasserstein distance Wpε. result Achieves strong robust estimation guarantees under the Huber ε-contamination model. InfoOT improves data alignment by maximizing mutual information.
problem Optimal transport's limitations in handling clusters, outliers, and new data.
method InfoOT extends optimal transport by maximizing mutual information while minimizing distances.
result InfoOT outperforms optimal transport in domain adaptation, cross-domain retrieval, and single-cell alignment.
Non-negative matrix factorization (NMF) minimizes the Euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated CauchyNMF loss that handle outliers by truncating large e…
MTLRRC improves MTL by robustly clustering tasks and detecting outliers.
problem Improving MTL by handling outlier tasks and sharing common information.
method Robust regularized clustering with non-convex group penalties.
result MTLRRC effectively detects and clusters tasks, improving overall performance.
Proposes a robust FMR model for handling sample heterogeneity.
problem Handling sample heterogeneity with a single regression model.
method Clusters samples and jointly models multiple incomplete mixed-type targets.
result Achieves state-of-the-art performance on synthetic and real-world data.
New framework robustly handles outliers in Wasserstein DRO for better decision-making.
problem Non-geometric perturbations like adversarial outliers distort Wasserstein distance.
method Proposes an outlier-robust WDRO framework using a robust Wasserstein ball.
result Derives minimax optimal excess risk bounds for robust WDRO.
New framework detects out-of-distribution data by considering intrinsic ID attributes in outliers.
problem Deploying reliable machine learning systems requires effective out-of-distribution detection.
method Structured multi-view-based out-of-distribution detection learning (MVOL) framework.
result MVOL effectively utilizes both auxiliary OOD datasets and wild datasets with noisy in-distribution data.
Many sensors, such as range, sonar, radar, GPS and visual devices, produce measurements which are contaminated by outliers. This problem can be addressed by using fat-tailed sensor models, which account for the possibility of outliers. Unfortunately, all estimation algorithms belonging to the family of Gaussian filters…
Two-stage framework detects multi-modal outliers.
problem Detecting outliers in multi-modal data structures.
method Combines global kernel PCA and local clustering stages.
result Significantly outperforms existing methods on challenging datasets.
Unified framework TERM improves fairness and robustness.
problem Outliers and subgroup fairness in empirical risk minimization.
method Unified framework TERM with a hyperparameter tilt.
result TERM improves fairness and robustness.
Proposes a robust portfolio method for large asset universes.
problem Outliers in return data affect traditional portfolio optimizations.
method Robust PCA, shrinkage estimation, and adaptive portfolio weights.
result Superior portfolio performance in numerical and empirical tests.
Paper extends RPD for better handling multiple modalities and non-convexity.
problem Handling multiple modalities and non-convexity in data clouds.
method Computes RPD in a reproducing kernel Hilbert space using kernel principal component analysis.
result The method outperforms RPD and is comparable to other models on benchmark datasets.
Enhances robustness of MOGP regression for multiple correlated outputs.
problem Model misspecification and outliers in MOGP regression.
method Extends RCGP framework to multi-output setting.
result Provable robust MOGP with joint correlation capture.
R package tsrobprep cleans and prepares time series data robustly.
problem Inefficient methods for handling missing values and outliers in time series data.
method Model-based methods for imputation and outlier detection considering time series properties.
result Robust and tunable methods for data preprocessing in time series analysis.
New EM algorithm for mixtures of elliptical distributions handles missing data and outliers.
problem Missing data imputation for noisy and non-Gaussian data.
method Investigation of a new EM algorithm for mixtures of elliptical distributions.
result The proposed algorithm is robust to outliers and competitive with other methods.
Robustness to outliers is a central issue in real-world machine learning applications. While replacing a model to a heavy-tailed one (e.g., from Gaussian to Student-t) is a standard approach for robustification, it can only be applied to simple models. In this paper, based on Zellner's optimization and variational form…
The paper introduces a new FOR framework using Huber and ε-insensitive losses.
problem Handling outliers and sparsity in functional output regression.
method Proposes a flexible FOR framework with infimal convolution losses and computable algorithms.
result Demonstrates efficiency and effectiveness on synthetic and real-world data.
Effective and accurate model selection is an important problem in modern data analysis. One of the major challenges is the computational burden required to handle large data sets that cannot be stored or processed on one machine. Another challenge one may encounter is the presence of outliers and contaminations that da…
Improved DP SO with large Lipschitz parameters, handling outliers and heavy-tailed data.
problem Differential privacy in stochastic optimization with large Lipschitz parameters.
method Assumes bounded k-th order moments, provides linear-time algorithms for smooth convex and non-smooth convex losses.
result Improved risk bounds scaling with k-th moment, not uniform Lipschitz parameter.