An increasingly common setting in machine learning involves multiple parties, each with their own data, who want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents to make better predictions than they would individually, but may not be willing to release labeled data or model parameters.

Figure 5: State Supportfor RPBBAC (left) and SAC (right) in MountainCar -Continuous-v0.

As the state-of-the-art framework, i.e., RecEraser, naturally achieves full unlearning completeness,