OOD detection relying on pure visual information can inherit the limitations, e.g., when an OOD

This paper considers the problem of recovering the policies of multiple interacting experts by estimating their reward functions and constraints where the demonstration data of the experts is distributed to a group of learners. We formulate this problem as a distributed bi-level optimization problem and propose a novel bi-level "distributed inverse constrained reinforcement learning" (D-ICRL) algorithm that allows the learners to collaboratively estimate the constraints in the outer loop and learn the corresponding policies and reward functions in the inner loop from the distributed demonstrations through intermittent communications. We formally guarantee that the distributed learners asymptotically achieve consensus which belongs to the set of stationary points of the bi-level optimization problem. Simulations are done to validate the proposed algorithm.

From the problem setting perspective, OOD detection is closely related to neighboring fields including anomaly detection (AD), open set recognition (OSR), and model uncertainty, since methods developed for one domain are often applicable to each other.

The increasing reliance on ML models in high-stakes tasks has raised a major concern about fairness violations.

All relaxations are optimized via our Lagrangian framework. All code was implemented using PyTorch, and optimized using L-BFGS. On the right, the difference framework is used to achieve equality of opportunity on COMP AS. We set the initial learning rate 0.1, which was Here we define equality of opportunity on false negative rates, i.e. predicting that someone Setting s = b, however, causes the linear relaxation to degenerate. For our deep learning experiments, we used the approach of Sec.

Machine learning is also increasingly used by law enforcement, where ethical questions become civil rights questions.