Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles

1 code implementation • 7 Mar 2023 • Zhiwei Tang, Dmitry Rybin, Tsung-Hui Chang

In this study, we delve into an emerging optimization challenge involving a black-box objective function that can only be gauged via a ranking oracle-a situation frequently encountered in real-world scenarios, especially when the function is evaluated by human judges.

Image Generation reinforcement-learning +1

Invariant Layers for Graphs with Nodes of Different Types

no code implementations • 27 Feb 2023 • Dmitry Rybin, Ruoyu Sun, Zhi-Quan Luo

We further narrow the invariant network design space by addressing a question about the sizes of tensor layers necessary for function approximation on graph data.