According to the paper "Learning to Optimize Tensor Programs", it seems that Bayesian Optimization is not a good choice as a tuner because of the reasons shown below. 1. Uncertainty estimation was not as important in autotuning problem, possibly because the models were trained with more training samples than traditional hyper-parameter optimization problems. 2. Configuration space s is not invarient which makes Bayesian Optimization not working on transfer learning.
Am I correct? I took screenshots of the paragraphs in the paper.   So Bayesian Optimization do not work well on auto tuning tasks, why It was mentioned in the last section of the paper?  --- [Visit Topic](https://discuss.tvm.apache.org/t/autotvm-question-about-bayesian-optimization/10852/1) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/437c5954f3475961e95303e89f09a9d513621c39588ffcb71b0f59a7fa000276).
