We provide a differentially private algorithm for hypothesis selection.

Transferring prior experience to new tasks is central to an agent's adaptability. In this work, we aim to accelerate online reinforcement learning by leveraging prior experience from large offline data.

Most of the existing work [2, 5, 6, 7, 8] for nonconvex SCO problem is based on the assumption that both functionsf()andg()are smooth.

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

Additionally,wepropose theGraphCroc, anewGAE thatsupports flexible encoder architectures tailored forvarious downstream tasksand ensures robust structural reconstruction, through a mirrored encoding-decoding process.