Bayesian optimization (BO) is a popular approach to optimize expensive-toevaluate black-box functions. A significant challenge in BO is to scale to highdimensional parameter spaces whileretaining sample efficiency. Asolution considered in existing literature is to embed the high-dimensional space in a lowerdimensional manifold, often via a random linear embedding.

We propose a new paradigm to construct code prompts and successfully uncover social biases in code generation models.

Valve's Steam Machine: Everything we know A roundup of recent reviews published by Engadget. We're starting to hit our stride in 2026. Now that February is here, our reviews team is flush with new devices to test, which means you've got a lot to catch up on if you haven't been following along. Read on for a roundup of the most compelling new gear we've tested recently from gaming, PCs, cameras and more. The Nex Playground brings motion-tracked games to the entire family.

Nevertheless, despite theproliferation ofresearch onalgorithmic fairness inrecent years, veryfew methods exist that can handle multiclass classification tasks with non-binary sensitive attributes.

Nevertheless, despite theproliferation ofresearch onalgorithmic fairness inrecent years, veryfew methods exist that can handle multiclass classification tasks with non-binary sensitive attributes.