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Found 7 papers, 1 papers with code
Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Specifically, we propose to leverage causal knowledge by regarding the distributional shifts in subpopulations and deployment environments as the results of interventions on the underlying system.
Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring
We formally prove that networks with a small Lipschitz modulus (which corresponds to a low susceptibility to adversarial attacks) map data points closer to the cluster centroids, which results in a mapping to a k-means-friendly space.
k is the Magic Number -- Inferring the Number of Clusters Through Nonparametric Concentration Inequalities
In this paper, we strive to determine the number of clusters by answering a simple question: given two clusters, is it likely that they jointly stem from a single distribution?
The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering
When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density.
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data
The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, where there exists confounding bias; on the other hand, we have to deal with the identification of CATE when the distribution of covariates in treatment and control groups are imbalanced.
Controversy Rules - Discovering Regions Where Classifiers (Dis-)Agree Exceptionally
Finding regions for which there is higher controversy among different classifiers is insightful with regards to the domain and our models.
Subjectively Interesting Subgroup Discovery on Real-valued Targets
The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes.
