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Found 25 papers, 11 papers with code
On Constrained Spectral Clustering and Its Applications
Furthermore, by inheriting the objective function from spectral clustering and encoding the constraints explicitly, much of the existing analysis of unconstrained spectral clustering techniques remains valid for our formulation.
Stochastic Coordinate Coding and Its Application for Drosophila Gene Expression Pattern Annotation
The effectiveness of gene expression pattern annotation relies on the quality of feature representation.
Some Advances in Role Discovery in Graphs
Role discovery in graphs is an emerging area that allows analysis of complex graphs in an intuitive way.
Dense Transformer Networks
The proposed dense transformer modules are differentiable, thus the entire network can be trained.
Transfer Regression via Pairwise Similarity Regularization
Transfer learning methods address the situation where little labeled training data from the "target" problem exists, but much training data from a related "source" domain is available.
Probabilistic Formulations of Regression with Mixed Guidance
Regression problems assume every instance is annotated (labeled) with a real value, a form of annotation we call \emph{strong guidance}.
Extreme Learning to Rank via Low Rank Assumption
We consider the setting where we wish to perform ranking for hundreds of thousands of users which is common in recommender systems and web search ranking.
On The Equivalence of Tries and Dendrograms - Efficient Hierarchical Clustering of Traffic Data
The widespread use of GPS-enabled devices generates voluminous and continuous amounts of traffic data but analyzing such data for interpretable and actionable insights poses challenges.
The Cluster Description Problem - Complexity Results, Formulations and Approximations
Since the descriptors/tags were not given to the clustering method, this is not a semi-supervised learning situation.
A Framework for Deep Constrained Clustering -- Algorithms and Advances
The area of constrained clustering has been extensively explored by researchers and used by practitioners.
Towards Fair Deep Clustering With Multi-State Protected Variables
Fair clustering under the disparate impact doctrine requires that population of each protected group should be approximately equal in every cluster.
Dense Transformer Networks for Brain Electron Microscopy Image Segmentation
The proposed dense transformer modules are differentiable, thus the entire network can be trained.
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Coverage-based Outlier Explanation
Existing algorithms are primarily focused on detection, that is the identification of outliers in a given dataset.
A Graph-Based Approach for Active Learning in Regression
Most existing active learning for regression methods use the regression function learned at each active learning iteration to select the next informative point to query.
Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability
Improving the explainability of the results from machine learning methods has become an important research goal.
Block Model Guided Unsupervised Feature Selection
Existing efforts for unsupervised feature selection on attributed networks have explored either directly regenerating the links by solving for $f$ such that $f(\mathbf{y}_i,\mathbf{y}_j) \approx \mathbf{A}_{i, j}$ or finding community structure in $\mathbf{A}$ and using the features in $\mathbf{Y}$ to predict these communities.
Towards Fair Deep Anomaly Detection
Anomaly detection aims to find instances that are considered unusual and is a fundamental problem of data science.
A Framework for Deep Constrained Clustering
A fundamental strength of deep learning is its flexibility, and here we explore a deep learning framework for constrained clustering and in particular explore how it can extend the field of constrained clustering.
Deep Descriptive Clustering
However, much modern machine learning focuses on complex data such as images, text, and graphs where deep learning is used but the raw features of data are not interpretable.
Deep Fair Discriminative Clustering
Deep clustering has the potential to learn a strong representation and hence better clustering performance compared to traditional clustering methods such as $k$-means and spectral clustering.
Explainable Clustering via Exemplars: Complexity and Efficient Approximation Algorithms
Explainable AI (XAI) is an important developing area but remains relatively understudied for clustering.
Towards Auditing Unsupervised Learning Algorithms and Human Processes For Fairness
Existing work on fairness typically focuses on making known machine learning algorithms fairer.
Scalable Spectral Clustering with Group Fairness Constraints
In this paper, we present a scalable algorithm for spectral clustering (SC) with group fairness constraints.
Cooperative Knowledge Distillation: A Learner Agnostic Approach
Knowledge distillation is a simple but powerful way to transfer knowledge between a teacher model to a student model.
Identification and Uses of Deep Learning Backbones via Pattern Mining
Here we explore the notion of identifying a backbone of deep learning for a given group of instances.
