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Found 16 papers, 1 papers with code
A Vision for Semantically Enriched Data Science
The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection.
Consistent Range Approximation for Fair Predictive Modeling
This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data.
Community Recovery in the Geometric Block Model
We show that a simple triangle-counting algorithm to detect communities in the geometric block model is near-optimal.
How to Design Robust Algorithms using Noisy Comparison Oracle
Metric based comparison operations such as finding maximum, nearest and farthest neighbor are fundamental to studying various clustering techniques such as $k$-center clustering and agglomerative hierarchical clustering.
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals
There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that aims to reduce the opaqueness of AI-based decision-making systems, allowing humans to scrutinize and trust them.
Learning to Generate Fair Clusters from Demonstrations
Empirical evaluation on three real-world datasets demonstrates the effectiveness of our approach in quickly identifying the underlying fairness and interpretability constraints, which are then used to generate fair and interpretable clusters.
Semantic Annotation for Tabular Data
We propose $C^2$, a column to concept mapper that is based on a maximum likelihood estimation approach through ensembles.
Causal Feature Selection for Algorithmic Fairness
In this work, we consider fairness in the integration component of data management, aiming to identify features that improve prediction without adding any bias to the dataset.
Adaptive Rule Discovery for Labeling Text Data
At the same time, creating a labeled subset of the data can be costly and even infeasible in imbalanced settings.
Fair Correlation Clustering
We consider two variations of fairness constraint for the problem of correlation clustering where each node has a color, and the goal is to form clusters that do not over-represent vertices of any color.
Balancing the Tradeoff Between Clustering Value and Interpretability
The interpretability of the clusters is complemented by generating simple explanations denoting the feature values of the nodes in the clusters, using frequent pattern mining.
Lexicographically Ordered Multi-Objective Clustering
We introduce a rich model for multi-objective clustering with lexicographic ordering over objectives and a slack.
Connectivity in Random Annulus Graphs and the Geometric Block Model
Our next contribution is in using the connectivity of random annulus graphs to provide necessary and sufficient conditions for efficient recovery of communities for {\em the geometric block model} (GBM).
The Geometric Block Model
To capture the inherent geometric features of many community detection problems, we propose to use a new random graph model of communities that we call a Geometric Block Model.
Fairness Testing: Testing Software for Discrimination
This paper defines software fairness and discrimination and develops a testing-based method for measuring if and how much software discriminates, focusing on causality in discriminatory behavior.
Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models
In this paper, we propose a holistic solution to the influence maximization (IM) problem.
Social and Information Networks Databases H.2.8
