Artificial intelligence (AI) systems possess significant potential to drive societal progress. However, their deployment often faces obstacles due to substantial safety concerns.

In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process.

Recall that ˆmc(u) is exactly the listener's decoder in the IB framework (see Section 3.1.1). Therefore, anyother decoder would lend an upper bound on the informativeness loss term. Notice that under our assumptions,ˆmc is a Gaussian mixture, whereas the speaker's beliefs are simply Gaussian. All the systems with the samek form an equivalence class and the canonical system within each class is the one with minimalk. These canonical systems are the natural one to prefer, because they can attain the optimum for a given complexity with aminimal codebook.

Here, we integrate these two approaches by trading off utility, informativeness, and complexity in EC.

Distributed multi-task learning provides significant advantages in multi-agent networkswithheterogeneous datasources where agents aimtolearndistinctbut correlated models simultaneously. However, distributed algorithms for learning relatedness among tasks arenotresilient inthepresence ofByzantine agents. In this paper, we present an approach for Byzantine resilient distributed multi-task learning. We propose an efficient online weight assignment rule by measuring the accumulated loss using an agent's data and its neighbors' models. A small accumulated loss indicates a large similarity between the two tasks.