However, simply pooling everyone's data and sharing with each other can lead to free-riding [

In fact, in a particular limit, the optimal path is arithmetic.

The high-level architecture of our simulator is illustrated in Figure 1 of Section 4. Additional details (with references to objects in the source code) are provided below. Simulations were run in Python 3.8 on an Intel(R) Xeon(R) CPU E5-2667 One direction is to extend the feature description of the ads (beyond topic) to include features that reflect ad quality and location. Baseline parameters are: µ = 0 . The resulting cohort errors are consistent with Figure 1 of Section 5.1. For the fully informative prior, the agent is completely certain of users' cohorts for Lastly, for the uninformative prior, revelation of a user's cookie does not inform the The agent's ability to distinguish users based on their responses depends on the similarities of affinities across users in different cohorts.

How does a user's consent decision influence

The Appendix is organized as follows.

In active learning (AL), we focus on reducing the data annotation cost from the model training perspective. However, "testing", which often refers to the model