FLAME auto-labels mobile data efficiently on diverse processors.
problem Accurately and efficiently labeling mobile data with unknown labels on heterogeneous processors.
method Self-adaptive auto-labeling system Flame that schedules and executes workloads on mobile processors.
result Flame achieves high labeling accuracy and performance on heterogeneous mobile processors.
ATLAS separates invariant and transferable latent factors across diverse environments.
problem Transfer learning and robust prediction in heterogeneous environments.
method ATLAS leverages invariance principle to disentangle latent factors and uses auxiliary labels for robust prediction.
result Near-oracle performance and robust transferable prediction in new environments.
Study on federated learning with private label sets, showing privacy benefits without significant accuracy loss.
problem Effects of label set heterogeneity and privacy constraints in federated learning.
method Apply classical classifier combination methods and adapt FL methods for private label sets, compare public and private settings.
result Reducing labels harms model performance, but centralized tuning can help.
Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with such problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this pap…
CAP adapts optimization to class attributes for better fairness.
problem Heterogeneities across classes impede classification performance.
method CAP generates class-specific learning strategies based on attributes.
result CAP improves over naive approach and is competitive with prior art.
New algorithms improve Gaussian mixture model estimation in DFL with heterogeneous data.
problem Bias in EM algorithm for Gaussian mixtures in DFL with heterogeneous data.
method MNEM and semi-MNEM algorithms integrating historical and partially labeled data.
result MNEM and semi-MNEM achieve asymptotic efficiency and improved convergence.
Paper analyzes D-SGD convergence with heterogeneous data and proposes topology learning.
problem Efficiently dealing with data heterogeneity in decentralized learning.
method Revisits D-SGD analysis, introduces neighborhood heterogeneity, and proposes topology learning.
result Formulates topology learning as a tractable optimization problem and demonstrates its effectiveness.
Safety-critical applications require machine learning models that output accurate and calibrated probabilities. While uncalibrated deep networks are known to make over-confident predictions, it is unclear how model confidence is impacted by the variations in the data, such as label noise or class size. In this paper, w…
We address the problem of semi-supervised learning in relational networks, networks in which nodes are entities and links are the relationships or interactions between them. Typically this problem is confounded with the problem of graph-based semi-supervised learning (GSSL), because both problems represent the data as …
A new method improves semi-supervised learning by handling tasks with different attribute spaces.
problem Existing methods assume tasks share the same attribute space, limiting their applicability.
method Meta-learning approach that embeds labeled and unlabeled data in task-specific spaces using neural networks.
result Improves test performance on tasks with small labeled data using unlabeled and various task data.
We study the misclassification error for community detection in general heterogeneous stochastic block models (SBM) with noisy or partial label information. We establish a connection between the misclassification rate and the notion of minimum energy on the local neighborhood of the SBM. We develop an optimally weighte…
New method reduces labeler costs by aggregating predictions from local classifiers.
problem Reduce labeler costs in multiclass classification.
method Model K-class classification using smaller classifiers trained on subsets of tasks. result Near-optimal scheme for designing classifier configurations reduces labeler costs.
A novel transfer learning framework combines multiple data sources for PU learning.
problem Challenges in PU learning due to lack of negative labels and data scarcity.
method Model averaging of heterogeneous data sources, including binary labeled, semi-supervised, and PU data.
result Method outperforms other methods in predictive accuracy and robustness, especially under limited labeled data.
Response time improves alignment with diverse human preferences.
problem Standard aggregation of feedback ignores heterogeneity and anonymity.
method Augmenting feedback with response time data and modeling decisions with DDM.
result Estimator of heterogeneous preferences converges to true average preference.
S2M optimizes mining for diverse data subpopulations.
problem Scalability and uniformity in training sets with many labels and diverse data.
method Doubly-stochastic mining (S2M) computes per-example and minibatch losses on hardest labels/examples.
result S2M ensures good performance across all data subpopulations.
CWAN tackles multi-source heterogeneous domain adaptation with conditional weighting.
problem Learning cross-domain samples from multiple heterogeneous domains.
method CWAN uses a feature transformer, label classifier, and domain discriminator to learn from multiple sources.
result CWAN outperforms state-of-the-art methods on four real-world datasets.
Many real-world datasets are labeled with natural orders, i.e., ordinal labels. Ordinal regression is a method to predict ordinal labels that finds a wide range of applications in data-rich domains, such as natural, health and social sciences. Most existing ordinal regression approaches work well for independent and id…
FedReLa: A novel data-level approach for imbalanced federated learning
problem Improving accuracy of federated learning models under class imbalance and data heterogeneity
method Re-labeling samples with a feature-dependent label re-allocator
result Significant improvements in accuracy for minority classes and overall accuracy on stepwise-imbalanced and long-tailed datasets
Extracts geometric information from point-clouds for multiclass classification.
problem Multiclass Classification with labeled point-clouds.
method Stochastic partial orderings and label embedding trees.
result Computes multiscale geometries for explainable prediction and error-free labeling.
Community detection was a hot topic on network analysis, where the main aim is to perform unsupervised learning or clustering in networks. Recently, semi-supervised learning has received increasing attention among researchers. In this paper, we propose a new algorithm, called weighted inverse Laplacian (WIL), for predi…
New taxonomy reveals different detection limits for various types of fraud.
problem Existing fraud detection treats all fraud as the same, ignoring its diverse forms.
method Introduced an observation-mechanism taxonomy with five fraud classes.
result Separate estimation by fraud class outperforms pooled estimation.
FLAIR dataset for federated learning benchmarks.
problem Lack of suitable federated learning datasets.
method Curated large-scale annotated image dataset for multi-label classification.
result FLAIR captures real-world federated learning challenges.
New algorithm detects community labels in networks using unlabeled data.
problem Detect community labels in networks with partially labeled data.
method Proposes an algorithm using structural similarity metrics.
result Theoretical guarantees for misclassification error.
PanRep learns universal node embeddings for heterogeneous graphs.
problem Learning universal node embeddings for heterogeneous graphs.
method Graph Neural Network (GNN) model with four decoders capturing different properties.
result PanRep outperforms unsupervised and supervised methods in node classification and link prediction.
Flexible model captures varying scales in data clusters.
problem Real-world data often exhibits varying scales or intensities, violating the homogeneity assumption of classical Gaussian mixture models.
method Individual-heterogeneous sub-Gaussian mixture model with an efficient spectral method for exact recovery.
result The method provably achieves exact recovery of true cluster labels under mild separation conditions.
Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain. In this paper, we propose a Soft Transfer Network (STN), which jointly learns a domain-shared classifier and a domain-invariant subspace in an end-to-end manner, for a…
HeteGCN improves text classification with efficient, scalable graph models.
problem Text classification with large datasets and features, especially in small labeled sets.
method HeteGCN combines PTE and TextGCN, using heterogeneous graphs and feature embeddings.
result HeteGCN achieves better performance and scalability compared to existing methods.
GDA-HIN adapts across heterogeneous networks by aligning shared and private node types.
problem Domain adaptation challenges in heterogeneous networks with shared and private node types.
method Generalized Domain Adaptive model across HINs (GDA-HIN) that aligns identical-type nodes and edges while utilizing different-type nodes and edges.
result GDA-HIN outperforms state-of-the-art methods in various domain adaptation tasks across heterogeneous networks.
Self-training improves model accuracy by refining pseudo-labels.
problem Improving semi-supervised learning with self-training.
method Theoretical insights into self-training algorithm with a focus on linear classifiers.
result Self-training iterations can improve model accuracy even if stuck in sub-optimal fixed points.
This work identifies and mitigates topological bias in HGNNs using meta-weighting and debiasing.
problem Topological bias in HGNNs affects model performance on specific nodes.
method Meta-weighting adjacency matrix, PageRank projection, debiasing structure.
result The debiasing structure improves HGNNs' performance and debiasing.
Study approximates operators on labelled conditional distributions for non-exchangeable systems.
problem Approximating operators on constrained probability measures for non-exchangeable systems.
method Combines cylindrical approximations and DeepONet-type neural architecture for finite-dimensional representations.
result Establishes a universal approximation theorem for continuous operators on Mλ. A new method for clustering heterogeneous data using likelihood-adjusted SDP.
problem Clustering heterogeneous data with different cluster shapes and sizes.
method Iterative likelihood-adjusted semidefinite programming (iLA-SDP) method.
result iLA-SDP achieves lower mis-clustering errors compared to other methods.
Proposes a progressive label correction method for feature-dependent label noise.
problem Real-world large-scale datasets often suffer from heterogeneous, feature-dependent label noise.
method A progressive label correction algorithm that iteratively refines the model.
result A classifier trained with this strategy converges to be consistent with the Bayes classifier for various noise patterns.
Transfer learning improves loan recovery rate forecasting under data scarcity.
problem Data scarcity in loan portfolios limits RR modeling accuracy.
method Introduces FT-MDN-Transformer, a mixture-density tabular Transformer architecture for TL.
result FT-MDN-Transformer outperforms baseline models in RR forecasting, especially under covariate and conditional shifts.
A number of modern learning tasks involve estimation from heterogeneous information sources. This includes classification with labeled and unlabeled data as well as other problems with analogous structure such as competitive (game theoretic) problems. The associated estimation problems can be typically reduced to solvi…
While label fusion from multiple noisy annotations is a well understood concept in data wrangling (tackled for example by the Dawid-Skene (DS) model), we consider the extended problem of carrying out learning when the labels themselves are not consistently annotated with the same schema. We show that even if annotators…
Proposes a method to improve graph neural networks on heterogeneous graphs using meta-paths.
problem Improving graph neural networks on heterogeneous graphs with auxiliary tasks.
method Self-supervised auxiliary learning with meta-paths for heterogeneous graphs.
result Consistently improves link prediction and node classification on heterogeneous graphs.
Tissue microarray (TMA) images have been used increasingly often in cancer studies and the validation of biomarkers. TACOMA---a cutting-edge automatic scoring algorithm for TMA images---is comparable to pathologists in terms of accuracy and repeatability. Here we consider how this algorithm may be further improved. Ins…
A novel framework interprets driving patterns using Action phases clustering.
problem Challenges in comprehending driving heterogeneity from underlying behavior mechanisms.
method Resampling and Downsampling Method (RDM) followed by iterative clustering calibration.
result Six driving patterns identified in real-world datasets, revealing dynamic nature of driving.
Conventional mutual information (MI) based feature selection (FS) methods are unable to handle heterogeneous feature subset selection properly because of data format differences or estimation methods of MI between feature subset and class label. A way to solve this problem is feature transformation (FT). In this study,…
This paper studies how label noise affects Federated Learning.
problem The impact of label noise on Federated Learning.
method The paper derives an upper bound for the generalization error and conducts experiments on MNIST and CIFAR-10 datasets.
result The global model accuracy decreases linearly with increasing label noise, consistent with theoretical analysis.
SUOD accelerates OD for large, diverse models.
problem Training and scoring new samples with many unsupervised, heterogeneous OD models.
method Data reduction, model approximation, and taskload optimization.
result SUOD accelerates OD for over 20 benchmark datasets and a real-world case.
Collective classification has been intensively studied due to its impact in many important applications, such as web mining, bioinformatics and citation analysis. Collective classification approaches exploit the dependencies of a group of linked objects whose class labels are correlated and need to be predicted simulta…
Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which …
Acquiring ground truth labels for unlabelled data can be a costly procedure, since it often requires manual labour that is error-prone. Consequently, the available amount of labelled data is increasingly reduced due to the limitations of manual data labelling. It is possible to increase the amount of labelled data samp…
We study the problem of identifying different behaviors occurring in different parts of a large heterogenous network. We zoom in to the network using lenses of different sizes to capture the local structure of the network. These network signatures are then weighted to provide a set of predicted labels for every node. W…
FPPI selectively uses predictions to improve inference efficiency.
problem Improving statistical inference with limited labeled data and heterogeneous prediction quality.
method Filtered Prediction-Powered Inference (FPPI) framework.
result FPPI achieves strictly improved asymptotic efficiency compared to existing methods.
ModSSC unifies semi-supervised classification for various data types.
problem Fragmented support for semi-supervised classification across different methods, settings, and data types.
method ModSSC is a modular Python framework that supports reproducible and controlled experimentation for semi-supervised classification on heterogeneous data.
result ModSSC enables systematic comparison of semi-supervised learning across various datasets and model backbones.