arXiv Machine Learning
Jun 3
ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning -- Additional Material
arXiv:2605. 30225v2 Announce Type: replace Abstract: Clustering is an unsupervised technique for grouping data points by similarity.
Counterfactuals for Feature-Weighted Clustering
arXiv:2607. 14719v1 Announce Type: new Abstract: Counterfactual explanations provide local, interpretable insight by identifying changes to an input that would alter its assigned outcome.
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arXiv Machine Learning
Jun 4
A Geometric View of Counterfactual Behavior: Interaction of Boundary Proximity and Local Support
arXiv:2606. 04209v1 Announce Type: new Abstract: Counterfactual explanations seek small, semantically meaningful changes to an input that alter a model's prediction, and are widely used to interpret and audit machine learning systems.
arXiv Machine Learning
Sep 18
FCx: An algorithm for finding Feasible Counterfactual Explanations
FCx is a new algorithm that generates counterfactual explanations while explicitly enforcing feasibility constraints. It uses a modified Variational Autoencoder with a multi‑factor loss to produce realistic, low‑cost counterfactuals that satisfy both hard constraints supplied by users and soft constraints inferred via causal inference. Experiments on four public datasets demonstrate that FCx matches state‑of‑the‑art performance across multiple metrics while guaranteeing feasibility.
Hugging Face Trending Papers
Sep 16
FCx: An algorithm for finding Feasible Counterfactual Explanations
Counterfactual (CF) explanations identify changes that alter an input's classification. While existing methods produce realistic and low-cost CFs, they often fail to ensure feasibility, by suggesting...
arXiv Machine Learning
Jul 27
CEL: Comprehensive Counterfactual Explanations Library and Benchmark
arXiv:2607. 22045v1 Announce Type: new Abstract: Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's prediction to a desired outcome.
arXiv Machine Learning
Jun 15
Cluster LOCO: Feature Importance For Interpreting Clusters
arXiv:2606. 14592v1 Announce Type: cross Abstract: Clustering is widely used for exploratory analysis and scientific discovery, driving insights from market segmentation to biological data analysis, but its outputs can be difficult to interpret, audit, and reproduce as modern datasets become increasingly large and complex.
arXiv AI
Jun 4
ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation
arXiv:2509. 25289v4 Announce Type: replace-cross Abstract: Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue.
arXiv Machine Learning
Jul 7
Sensitivity Sampling with Predictions for k-Means Clustering
arXiv:2607. 04949v1 Announce Type: new Abstract: We study the problem of k-means clustering on large datasets.
arXiv AI
Sep 10
AVCG: A Generalized Variational Framework for Counterfactual Generation under Hypothesis Distributions
arXiv:2609.07917v1 Announce Type: cross Abstract: Counterfactual explanations formalize "what-if" scenarios by identifying modifications to an input instance that obtain a desired alternative predict...
arXiv Computation and Language
Sep 22
Revisiting Lexicon Evaluation in Unsupervised Word Discovery
The paper critiques the normalized edit distance metric used for evaluating lexicons derived from unsupervised word discovery, noting its bias toward large clusters and its failure to account for the distribution of true classes across clusters. It proposes two new metrics—one that weights cluster size when measuring within‑cluster consistency and another that evaluates how true words are spread across clusters—drawing on clustering theory. Experiments on synthetic and real‑world lexicons show that these combined metrics better correlate with ground‑truth distributions and are more robust to evaluation biases.
arXiv Machine Learning
Jun 5
Central Description Length (CDL) Clustering Validation Index
arXiv:2606. 05230v1 Announce Type: cross Abstract: Selecting a clustering algorithm and its hyperparameters without labels is a common difficulty in engineering machine learning pipelines that work with unsupervised analysis of sensor, image, or process data.
arXiv Machine Learning
Aug 27
Towards A Unified Information Bottleneck Framework for Time Series Explanations
arXiv:2608. 25897v1 Announce Type: new Abstract: Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior.