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Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation
In order to enhance the ability of FAMs to distinguish different features' contributions in this challenging setting, we propose to utilize the Probability of Necessity and Sufficiency (PNS) that perturbing a feature is a necessary and sufficient cause for the prediction to change as a measure of feature importance.
Leveraging Task Structures for Improved Identifiability in Neural Network Representations
This work extends the theory of identifiability in supervised learning by considering the consequences of having access to a distribution of tasks.
Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning
The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria.
Towards Interpretable Molecular Graph Representation Learning
Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks.
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
We benchmark LaPool on molecular graph prediction and understanding tasks and show that it outperforms recent GNNs.
