Inmultiagent domains, coping withnon-stationary agents thatchange behaviors from time to time is a challenging problem, where an agent is usually required to be able to quickly detect the other agent's policy during online interaction, and then adapt its own policy accordingly.

We now move to the misspecified case.

Heuristic tools from statistical physics have been used in the past to locate the phase transitions and compute the optimal learning and generalization errors in the teacher-student scenario in multi-layer neural networks. In this contribution, we provide a rigorous justification of these approaches for a two-layers neural network model called the committee machine. We also introduce a version of the approximate message passing (AMP) algorithm for the committee machine that allows to perform optimal learning in polynomial time for a large set of parameters.

Neural Information Processing Systems http://nips.cc/

SSL based on persistent homology (PH), a mathematical tool for modeling topological features of data that persist across multiple scales.

The significant difference between the deployment-time partners' distribution and the training partners' distribution determined by the

Implementation of our proposed method and the datasets are now available1.