This paper reviews resampling techniques to improve classification in imbalanced datasets.
problem Improving classification performance in datasets with class imbalance.
method Review and study of various resampling techniques.
result Effectiveness of resampling techniques on classification performance.
New algorithm solves unbalanced optimal transport on trees in quasi-linear time.
problem Efficiently solving unbalanced optimal transport problems on trees.
method Proposed an algorithm that solves a more general unbalanced optimal transport problem exactly in quasi-linear time on a tree metric.
result Solves unbalanced optimal transport on trees in quasi-linear time (less than one second for a tree with one million nodes).
Unbalanced GANs stabilize GAN training by pre-training the generator with VAE.
problem Stable training of GANs to avoid mode collapses and improve image quality.
method Pre-train GAN generator with VAE, balance generator and discriminator training, prevent discriminator's early convergence.
result Unbalanced GANs reduce mode collapses and outperform ordinary GANs in stability, convergence, and image quality.
ARIMA model detects credit card fraud in unbalanced datasets.
problem Unsupervised credit card fraud detection in unbalanced datasets.
method ARIMA model applied to customer spending patterns for anomaly detection.
result ARIMA model outperforms benchmark anomaly detection methods.
SI learning is effective for unbalanced data, showing resilience to object dependencies.
problem Learning from unbalanced data in multi-instance learning.
method Analysis of SI learning objective with rich classifier families, focusing on unbalanced data.
result Unbalanced data improves resilience of SI method to object dependencies, especially in neural networks.
Proposes a method to stabilize treatment effect estimation with unbalanced data.
problem Unbalanced treatment assignment leading to unstable propensity score estimations.
method Undersamples data for propensity score modeling and calibrates scores to match original distribution.
result The estimator retains asymptotic properties of the DML estimator and improves finite sample performance.
This paper uses UOT metrics for better dimensionality reduction and classification/clustering.
problem Improving dimensionality reduction and classification/clustering methods.
method Uses Hellinger--Kantorovich metric from unbalanced optimal transport (UOT).
result UOT outperforms Euclidean and OT-based methods in classification and clustering tasks.
Unbalanced minibatch Optimal Transport improves domain adaptation performance.
problem Optimal transport distances are computationally expensive for large datasets.
method Use unbalanced minibatch Optimal Transport to estimate distances over subsets of data.
result Unbalanced Optimal Transport leads to better domain adaptation results.
A new OCC model for unbalanced datasets with better interpretability.
problem Handling unbalanced datasets in OCC.
method Kernel density estimation within a tree-based learning algorithm (OC-Tree).
result The OC-Tree outperforms state-of-the-art methods on benchmark datasets.
CR-UOT improves matching of heterogeneous single-cell omics profiles.
problem Matching nonnegative finite Radon measures across heterogeneous spaces.
method Cost-regularized unbalanced optimal transport (CR-UOT) framework.
result CR-UOT improves alignment of heterogeneous single-cell omics profiles.
A new algorithm improves semi-supervised learning in unbalanced and heterogeneous networks.
problem Improving semi-supervised learning in partially labeled networks with unbalanced and heterogeneous data.
method Proposed a new algorithm called weighted inverse Laplacian (WIL) for partially labeled networks, based on random walk and information propagation.
result WIL ensures misclassification rate of order O(d1) for partially labeled degree-corrected block model (pDCBM) with average degree d=Ω(logn), and outperforms other methods in unbalanced and heterogeneous networks. Proposes a new spectral embedding norm for better cluster separation in unbalanced datasets.
problem Challenges traditional spectral clustering in unbalanced datasets, especially in anomaly detection.
method Introduces the spectral embedding norm, summing the squared values of the first I normalized eigenvectors. result Demonstrates improved performance in separating clusters from background in various datasets.
This study generates synthetic data to augment sleep apnea detection datasets.
problem Insufficient and unbalanced training datasets for health applications.
method Designing a recurrent Generative Adversarial Network to generate synthetic data and balance the dataset.
result All classifiers exhibit improved performance in sensitivity and kappa statistic.
Improved decision tree split selection to enhance accuracy in unbalanced datasets.
problem Bias in decision tree split selection, especially for unbalanced datasets.
method Proposed an updated gain ratio to correct bias and improve split selection.
result The updated gain ratio leads to better predictive accuracy in unbalanced datasets.
DRAGON improves learning for rare classes in unbalanced datasets using class descriptions.
problem Learning rare classes in unbalanced datasets with deep models.
method DRAGON is a late-fusion architecture that corrects bias towards frequent classes and fuses class-descriptions to improve tail-class accuracy.
result DRAGON outperforms state-of-the-art models on new benchmarks for long-tail learning with class descriptors.
Proposes a GAN-based model for semi-supervised text regression.
problem Predicting social and economic outcomes from unbalanced textual data.
method Conditional Generative Adversarial Network (GAN) for semi-supervised learning.
result End-to-end framework for predictions without high-level representation selection.
A new method for one-class classifiers using spanning trees and sub-spaces.
problem Designing efficient one-class classifiers for various datasets.
method Ensemble-of-classifiers approach, spanning trees, sub-spaces, minimum distance.
result Comparable and state-of-the-art performance on benchmark datasets.
Unbalanced COOT improves feature alignment robustly to outliers.
problem Optimal transport methods are sensitive to outliers in real-world data.
method COOT infers alignment between features and samples, unbalanced COOT adds robustness.
result Unbalanced COOT is robust to noise in real-world datasets.
Robotic agent learns new object classes incrementally with balanced training data.
problem Unbalanced training data when new objects are introduced.
method Incremental RLSC algorithm exploiting its structure for seamless class addition.
result Comparable or higher classification performance than batch RLSC, faster.
This study compares deep learning with other ML algorithms on credit scoring unbalanced data.
problem Training models on highly unbalanced data is challenging.
method Compared several machine learning algorithms with deep learning on a credit scoring unbalanced dataset.
result Deep learning shows promising performance on imbalanced data with little samples.
New method clusters strong and weak views effectively, improving performance by up to 40%.
problem Clustering incomplete multi-view data with unbalanced incompleteness.
method View evolution scheme and weighted multi-view subspace clustering.
result Improves clustering performance by up to 40% on three metrics.
New formula for instantaneous frequency in unbalanced systems.
problem Estimating frequency in unbalanced electrical systems.
method Utilizes affine differential geometry to link frequency and voltage derivatives.
result Proposes a new formula for instantaneous frequency estimation.
New methods estimate transport-growth pairs in unbalanced optimal transport.
problem Statistical guarantees for Monge-type estimation in unbalanced optimal transport remain limited.
method Developed two estimators for transport-growth pairs under different setups.
result Achieved minimax optimal rate for estimation of transport-growth pairs.
This paper generalizes Sinkhorn algorithm for unbalanced optimal transport.
problem Handling distributions with different total mass and robustness to outliers.
method Alternates between standard Sinkhorn updates and pointwise application of a contractive function.
result Defines Sinkhorn divergences that are differentiable, positive, definite, convex, and robust.
Paper presents scalable GAN-based method for unbalanced OT.
problem Scalable unbalanced optimal transport (OT).
method Generative adversarial networks (GANs) for unbalanced OT.
result Methodology scales well for high-dimensional continuous measures.
Paper addresses unbalanced data in common shock models for loss reserving.
problem Complications in capturing structural dependence with unbalanced data.
method Introduces a common shock Tweedie approach for unbalanced data.
result Better balance of common shock proportions and parsimonious solution.
A simple model for unbalanced optimal transport captures key features.
problem Capturing the main features of unbalanced optimal transport.
method Introducing a metric on the conical extension of diffeomorphisms and studying its properties.
result Total mass evolves with constant acceleration along geodesics.
Estimates mean and covariance for large, unbalanced stock returns panels.
problem Estimating mean and covariance in large, unbalanced panel data.
method Nonparametric, kernel-based joint estimator for conditional mean and covariance matrices.
result The idiosyncratic risk explains more than 75% of cross-sectional variance.
The paper shows conflict graphs of Petersen family graphs are mostly unbalanced.
problem Understanding the balance of conflict graphs in Petersen family graphs.
method Analyzing maximally planar subgraphs and their conflict graphs.
result All but three strong conflict graphs from Petersen Family Graphs are unbalanced.
Paper connects surface shape analysis and unbalanced optimal transport.
problem Computing the SRNF shape distance on piecewise linear surfaces.
method Characterizes SRNF shape distance as WFR distance pullback, proposes new algorithm for WFR distance computation.
result Direct computation of SRNF shape distance on piecewise linear surfaces.
Model shows how relaxed leverage can lead to asset price bubbles.
problem Understanding how financial leverage affects asset prices and growth.
method Developed a macro-finance model with feedback loops between investment and land prices.
result Relaxed leverage can cause unbalanced growth and asset price bubbles.
We extend Sobolev transport to unbalanced measures on graphs.
problem Optimal transport struggles with measures of different total mass and high computational complexity.
method We propose a scalable unbalanced Sobolev transport (UST) for measures on graphs.
result UST admits a closed-form formula for fast computation and is negative definite.
New explicit constructions of unbalanced Ramanujan bipartite graphs.
problem Constructing bipartite Ramanujan graphs with specified degrees and avoiding certain edges.
method Presented explicit constructions and discussed known methods for Ramanujan graph construction.
result Affirmative answer to constructing unbalanced Ramanujan bipartite graphs under certain conditions.
A new model corrects inhomogeneity in Optimal Transport with Boundary.
problem Inhomogeneity in UROT models for Optimal Transport with Boundary.
method Proposed a modified entropic regularization term to make UROT models homogeneous.
result Homogeneous UROT model preserves properties of standard UROT while correcting inhomogeneity.
Study of Gaussian distributions using entropic Gromov-Wasserstein and inner product Gromov-Wasserstein.
problem Optimal transportation between Gaussian distributions with different dimensions.
method Entropic Gromov-Wasserstein and inner product Gromov-Wasserstein, with closed-form expressions and von Neumann's trace inequality.
result Closed-form expressions for the entropic IGW and its unbalanced variant between Gaussian distributions.
Exact solutions reveal how unbalanced initializations promote rapid feature learning in neural networks.
problem Understanding how neural networks efficiently extract features from data.
method Deriving exact solutions to a minimal model of neural networks transitioning between lazy and rich learning regimes.
result Unbalanced layer-specific initialization variances and learning rates determine the degree of feature learning.
Proposes a Dual Focal Loss method to improve semantic segmentation on unbalanced datasets.
problem Improving performance on unbalanced datasets in semantic segmentation.
method Adaptive Class Weight based Dual Focal Loss (DFL) function.
result DFL improves convergence and reduces class imbalance effect.
New bounds for multi-task learning with varying task sizes.
problem Generalization in multi-task learning with tasks of different sizes.
method PAC-Bayesian bounds for unbalanced settings.
result Stronger generalization bounds for multi-task learning with varying task sizes.
Improved R-QDA classifier performs well in unbalanced data settings.
problem High sensitivity of R-QDA to covariance matrix estimation noise in unbalanced data.
method Proposes an improved R-QDA with two regularization parameters and a modified bias.
result Significantly better classification performance compared to traditional R-QDA.
Geodesic nets with three vertices have at most one balanced vertex.
problem Characterizing geodesic nets with specific vertex configurations.
method Analyzing geodesic nets on non-positively curved planes.
result Geodesic nets with three boundary vertices have at most one balanced vertex.
Review of unbalanced OT, entropic regularization, and GW for robust data comparison.
problem Lack of robustness, high computational costs, and difficulty in handling distinct spaces in OT.
method Unbalanced OT, entropic regularization, Gromov-Wasserstein distance.
result Efficient geometric loss functions for data sciences.
This paper introduces a new formulation of the Conic Gromov-Wasserstein distance for comparing complex network structures.
problem Comparing measures of unequal mass and complex network structures.
method Novel semi-coupling formulation and extension to hypernetworks.
result Establishes fundamental properties and robustness of CGW metric.
Unbalanced data arises in many learning tasks such as clustering of multi-class data, hierarchical divisive clustering and semisupervised learning. Graph-based approaches are popular tools for these problems. Graph construction is an important aspect of graph-based learning. We show that graph-based algorithms can fail…
Paper uses smart meter data to accurately estimate multi-phase topology and identify bus phases in unbalanced distribution grids.
problem Accurate topology knowledge is needed for monitoring and controlling uncertainties in unbalanced distribution grids.
method Converts multi-phase unbalanced systems into symmetrical components and uses information theory, power flow equations, and conditional independence relationships to estimate topology and identify bus phases.
result The algorithm accurately estimates multi-phase topology and identifies bus phases in unbalanced distribution grids, even with strong load unbalancing and DERs.
Characterizes distribution-free rates in unbalanced classification problems.
problem Minimizing error under two different distributions in unbalanced settings.
method Characterizes minimax rates over all pairs of distributions using a geometric condition.
result Identifies a dichotomy between hard and easy classes based on a three-points-separation condition.
Introduces new metric for Riemannian metrics, extending unbalanced optimal transport.
problem Extending unbalanced optimal transport to Riemannian metrics.
method Dynamic and static formulations of unbalanced optimal transport on Riemannian metrics.
result Wasserstein--Ebin metric provides a new Riemannian structure on the space of Riemannian metrics.
New scalable methods for unbalanced optimal transport improve efficiency and applicability.
problem Scalable algorithms for unbalanced optimal transport remain underexplored.
method Analysis of semi-dual formulation and adaptive gradient methods.
result SGD methods achieve a convergence rate of O(n/εT) for large-scale applications.
Paper introduces WFR metric for better document distance measurement.
problem Measuring semantic similarity between texts of different lengths.
method Apply Wasserstein-Fisher-Rao (WFR) metric from unbalanced optimal transport theory.
result WFR document distance significantly improves over WMD for texts of different lengths.