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Found 5 papers, 4 papers with code
dotears: Scalable, consistent DAG estimation using observational and interventional data
New biological assays like Perturb-seq link highly parallel CRISPR interventions to a high-dimensional transcriptomic readout, providing insight into gene regulatory networks.
Contrastive Mixup: Self- and Semi-Supervised learning for Tabular Domain
Recent literature in self-supervised has demonstrated significant progress in closing the gap between supervised and unsupervised methods in the image and text domains.
Marginal Contribution Feature Importance -- an Axiomatic Approach for The Natural Case
While it is common to make the distinction between local scores that focus on individual predictions and global scores that look at the contribution of a feature to the model, another important division distinguishes model scenarios, in which the goal is to understand predictions of a given model from natural scenarios, in which the goal is to understand a phenomenon such as a disease.
Explaining Groups of Points in Low-Dimensional Representations
A common workflow in data exploration is to learn a low-dimensional representation of the data, identify groups of points in that representation, and examine the differences between the groups to determine what they represent.
Model-Agnostic Feature Selection with Additional Mutual Information
We show that f-divergences provide a broad class of proper test statistics.
