Customer-obsessed science

Virtual machines (VM) form the foundation of modern cloud computing as they help logically abstract per-user compute from shared physical infrastructure. Users of these services require VMs of varying sizes and configurations, which the provider places on a set of physical machines (PMs). VMs on the same physical PM share memory and CPU resources, so a bad packing directly impacts the quality of user experience

Bayesian optimization (BO) is among the most effective and widely used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion encoded in an acquisition function, many of which are derived from the posterior predictive of a probabilistic surrogate model. Prevalent among these is the expected improvement (EI). Naturally, the need to ensure analytical tractability

In many real-world applications, the performance of machine learning models is evaluated not along a single objective, but across multiple, potentially competing ones. For instance, for a model deciding whether to grant or deny loans, it is critical to make sure decisions are fair and not only accurate. As it is often infeasible to find a single model performing best across all objectives, practitioners

Bayesian optimization (BO) is a popular method to optimize expensive blackbox functions. It efficiently tunes machine learning algorithms under the implicit assumption that hyperparameter evaluations cost approximately the same. In reality, the cost of evaluating different hyperparameters, be it in terms of time, dollars or energy, can span several orders of magnitude of difference. While a number of heuristics

We study offline policy evaluation in a setting where the target policy can take actions that were not available when the data was logged. We analyze the bias of two popular regression-based estimators in this setting, and upper-bound their biases by a quantity we refer to as the reward regression risk. We show that the estimators can be asymptotically unbiased and uniformly convergent if the reward regression