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Found 10 papers, 7 papers with code
Data-Efficient and Interpretable Tabular Anomaly Detection
In addition, the proposed framework, DIAD, can incorporate a small amount of labeled data to further boost anomaly detection performances in semi-supervised settings.
Extracting Expert's Goals by What-if Interpretable Modeling
Although reinforcement learning (RL) has tremendous success in many fields, applying RL to real-world settings such as healthcare is challenging when the reward is hard to specify and no exploration is allowed.
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
Deployment of machine learning models in real high-risk settings (e. g. healthcare) often depends not only on the model's accuracy but also on its fairness, robustness, and interpretability.
Towards Robust Classification Model by Counterfactual and Invariant Data Generation
Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlations to make predictions.
Ranked #1 on
Out-of-Distribution Generalization
on UrbanCars
How Interpretable and Trustworthy are GAMs?
Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning.
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
Models which estimate main effects of individual variables alongside interaction effects have an identifiability challenge: effects can be freely moved between main effects and interaction effects without changing the model prediction.
Dynamic Measurement Scheduling for Event Forecasting using Deep RL
We answer this question by deep reinforcement learning (RL) that jointly minimizes the measurement cost and maximizes predictive gain, by scheduling strategically-timed measurements.
Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL
We address the scheduling problem using deep reinforcement learning (RL) to achieve high predictive gain and low measurement cost, by scheduling fewer, but strategically timed tests.
Explaining Image Classifiers by Counterfactual Generation
We can rephrase this question to ask: which parts of the image, if they were not seen by the classifier, would most change its decision?
Dropout Feature Ranking for Deep Learning Models
Deep neural networks (DNNs) achieve state-of-the-art results in a variety of domains.
