We consider the general case where the per-formative risk can be non-convex, for which we develop efficient parameter-free optimistic optimization-based methods. Our algorithms significantly improve upon the existing Lips-chitz bandit-based method in many aspects. In particular, our framework does not require knowledge about the sensitivity parameter of the distribution map and the Lipshitz constant of the loss function. This makes our framework practically favorable, together with the efficient optimistic optimization-based tree-search mechanism. We provide experimental results that demonstrate the numerical superiority of our algorithms over the existing method and other black-box optimistic optimization methods.

The global DRAM (Dynamic Random Access Memory) market size is about tens of billions USD, and keeps increasing due to growing demand of DRAM in mobile devices, modern computers, selfdriving cars, etc. It is crucial to test DRAM using various workloads in verifying and guaranteeing DRAM quality. DRAM manufacturers utilize their known workloads for verification; however, it does not guarantee that DRAM works well for new workloads not known in advance. Therefore, it is necessary to detect new workloads to improve the quality of DRAM verification. The problem of detecting new workloads is formulated as an open-set recognition [19] task which classifies a test sample into the known classes or the unknown class, and identifies its class if it belongs to the known classes. A workload sequence contains a series of tuples with the command and the address information of memory accesses. To detect new workloads based on open-set recognition, we exploit a subsequence, a part of the entire sequence of a workload. Given a subsequence, we classify it into one of the known workload classes or identify it as the unknown class corresponding to new workloads.