5 accepted papers
Out-of-distribution (OOD) detection is the task of identifying inputs that deviate from the training data distribution. This capability is essential for the safe deployment of deep computer vision models in open-world environments. In this work, we propose a post-hoc method, Perturbation-Rectified O…
We consider the challenging problem of unsupervised change point detection in multivariate time series when the number of change points is unknown. Our method eliminates the user’s need for careful parameter tuning, enhancing its practicality and usability. Our approach identifies time series segmen…
We propose a Safe Pontryagin Differentiable Programming (Safe PDP) methodology, which establishes a theoretical and algorithmic framework to solve a broad class of safety-critical learning and control tasks---problems that require the guarantee of safety constraint satisfaction at any stage of the…
We consider the problem of modeling piece-wise stationary time series. We propose a new, data-driven technique to automatically identify change-points and learn piece-wise stationary models. We do not assume prior knowledge of the stationary models or the number of change points. Our method can auto…
This paper develops a Pontryagin differentiable programming (PDP) methodology, which establishes a unified framework to solve a broad class of learning and control tasks. The PDP distinguishes from existing methods by two novel techniques: first, we differentiate through Pontryagin's Maximum Princ…