Timezone: »

Model Inversion Networks for Model-Based Optimization
Aviral Kumar · Sergey Levine

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1745

This work addresses data-driven optimization problems, where the goal is to find an input that maximizes an unknown score or reward function given access to a dataset of inputs with corresponding scores. When the inputs are high-dimensional and valid inputs constitute a small subset of this space (e.g., valid protein sequences or valid natural images), such model-based optimization problems become exceptionally difficult, since the optimizer must avoid out-of-distribution and invalid inputs. We propose to address such problems with model inversion networks (MINs), which learn an inverse mapping from scores to inputs. MINs can scale to high-dimensional input spaces and leverage offline logged data for both contextual and non-contextual optimization problems. MINs can also handle both purely offline data sources and active data collection. We evaluate MINs on high- dimensional model-based optimization problems over images, protein designs, and neural network controller parameters, and bandit optimization from logged data.

Author Information

Aviral Kumar (UC Berkeley)
Sergey Levine (UC Berkeley)

More from the Same Authors