Deep learning practice is increasingly driven by powerful foundation models (FM), pre-trained at scale and then fine-tuned for specific tasks of interest.

Training data attribution (TDA) techniques find influential training data for the model's prediction on the test data of interest.

Then, we intend to calculate the constraint part. The algorithm for Licence method for single-target interventiion scenario is shown in Algorithm 1. The details of experimental baselines are demonstrated as follows. AIT [11] is an active learning method that utilize f-score to select intervention queries. REAL fidelity means the model always choose the highest fidelity to conduct experiments.

For example, to study the causal relations between the drugs and diseases, one can conduct clinical tests via selectively administering the medicines to the patients.

We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm.

Data augmentation has been proven effective for training high-accuracy convolu-tional neural network classifiers by preventing overfitting.

However, such a global learning rate schedule does not take into account the structural characteristics of neural networks (NNs).

Our primary focus is the identification of the latent structure in measurement models without parametric assumptions such as linearity or Gaussianity.