This section summarizes the different algorithms used for the Section 5 numerical studies. For all other benchmarks we use max_depth =3 and num_boost_rounds = 50 . ' and activate the deterministic Default values are used for all other hyperparameters. Figure 1 presents results of benchmark problems with known constraints. Domain bounds without decimals indicate integer-valued variable types.

Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data.

The checklist follows the references. For example: Did you include the license to the code and datasets? Please do not modify the questions and only use the provided macros for your answers. Checklist section does not count towards the page limit. Do the main claims made in the abstract and introduction accurately reflect the paper's Did you discuss any potential negative societal impacts of your work?

We consider the problem of fairly allocating sequentially arriving items to a set of individuals. For this problem, the recently-introduced P ACE algorithm leverages the dual averaging algorithm to approximate competitive equilibria and thus generate online fair allocations. P ACE is simple, distributed, and parameter-free, making it appealing for practical use in large-scale systems. However, current performance guarantees for P ACE require i.i.d.

The prompt-based learning paradigm has gained much research attention recently.

However, finding these directions usually requires an iterative optimization procedure over the space of projecting directions, which is computationally expensive. Moreover, the computational issue is even more severe in deep learning applications, where computing the distance between two mini-batch probability measures is repeated several times.