However, the generating process can be different in several aspects, such as the conditions and devices used for acquisition, different pre-processing, different compressions, etc. Domain adaptation

While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) applications, including the fine-tuning of generative models. Unfortunately, PPO requires multiple heuristics to enable stable convergence (e.g.

Figure 2: On the bottom we see a representation oftheproposed regression model that captures all the componentsτi of the mixture of Asymmetric Laplacian distributions (ALD)simultaneously.

If you are predicting the labely of a new object withˆy, how confident are you that y = ˆy?

Inthefinalresponseprediction,15 we treat all the neighbors equally, using the same weight. Their result also considered the runtime-precision tradeoff which we did not take into43 account.

Due to the non-convex nature of such joint estimators, the theoretical justification of their efficiency is typically challenging.