Recent empirical work shows that inconsistent results based on choice of hyperparameter optimization (HPO) configuration are a widespread problem in ML research. When comparing two algorithms J and K, searching one subspace can yield the conclusion that J outperforms K, whereas searching another can entail the opposite. In short, the way we choose hyperparameters can deceive us. In this talk, I will discuss work from NeurIPS 2020 in which we provide a theoretical complement to this prior empirical work, arguing that, to avoid such deception, the process of drawing conclusions from HPO should be made more rigorous. In this work, we name this process epistemic hyperparameter optimization (EHPO), and put forth a logical framework to capture its semantics and how it can lead to inconsistent conclusions about performance. Our framework enables us to prove EHPO methods that are guaranteed to be defended against deception, given a bounded compute time budget t. I will show how our framework is useful for proving and empirically validating a defended variant of random search, and close with broader takeaways concerning the future of robust HPO research.