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Found 15 papers, 3 papers with code
Adversarial Reweighting Guided by Wasserstein Distance for Bias Mitigation
To balance the data distribution between the majority and the minority groups, our approach deemphasizes samples from the majority group.
Causal Fairness-Guided Dataset Reweighting using Neural Networks
The two neural networks can approximate the causal model of the data, and the causal model of interventions.
Counterfactual Explanation for Regression via Disentanglement in Latent Space
In this paper, we introduce a novel method to generate CEs for a pre-trained regressor by first disentangling the label-relevant from the label-irrelevant dimensions in the latent space.
Interpretable Distribution-Invariant Fairness Measures for Continuous Scores
Here, we propose a distributionally invariant version of fairness measures for continuous scores with a reasonable interpretation based on the Wasserstein distance.
Counterfactual Explanation via Search in Gaussian Mixture Distributed Latent Space
In this paper, we introduce a new method to generate CEs for a pre-trained binary classifier by first shaping the latent space of an autoencoder to be a mixture of Gaussian distributions.
Explanation Shift: How Did the Distribution Shift Impact the Model?
Then, state-of-the-art techniques model input data distributions or model prediction distributions and try to understand issues regarding the interactions between learned models and shifting distributions.
Explanation Shift: Detecting distribution shifts on tabular data via the explanation space
We provide a mathematical analysis of different types of distribution shifts as well as synthetic experimental examples.
Dynamic Model Tree for Interpretable Data Stream Learning
Dynamic Model Trees are thus a powerful online learning framework that contributes to more lightweight and interpretable machine learning in data streams.
Robust Deep Neural Networks for Heterogeneous Tabular Data
Although deep neural networks (DNNs) constitute the state-of-the-art in many tasks based on image, audio, or text data, their performance on heterogeneous, tabular data is typically inferior to that of decision tree ensembles.
On Counterfactual Explanations under Predictive Multiplicity
In this work, we derive a general upper bound for the costs of counterfactual explanations under predictive multiplicity.
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
Feature selection can be a crucial factor in obtaining robust and accurate predictions.
Learning Model-Agnostic Counterfactual Explanations for Tabular Data
On one hand, we suggest to complement the catalogue of counterfactual quality measures [1] using a criterion to quantify the degree of difficulty for a certain counterfactual suggestion.
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees
We propose shallow model trees as a way to combine simple and highly transparent predictive models for higher predictive power without losing the transparency of the original models.
Combining Restricted Boltzmann Machines with Neural Networks for Latent Truth Discovery
Latent truth discovery, LTD for short, refers to the problem of aggregating ltiple claims from various sources in order to estimate the plausibility of atements about entities.
Restricted Boltzmann Machines for Robust and Fast Latent Truth Discovery
Despite a multitude of algorithms to address the LTD problem that can be found in literature, only little is known about their overall performance with respect to effectiveness (in terms of truth discovery capabilities), efficiency and robustness.
