Search Results for author:
Found 3 papers, 2 papers with code
Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models
Leveraging this approximation, we adapt the original model to eliminate information from the forgotten data at the representation level.
CaMU: Disentangling Causal Effects in Deep Model Unlearning
To address this shortcoming, the present study undertakes a causal analysis of the unlearning and introduces a novel framework termed Causal Machine Unlearning (CaMU).
Representing and Discovering Adversarial Team Behaviors Using Player Roles
In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
