Personalization has emerged as a critical research area in modern intelligent systems, focusing on mining users' behavioral history and adapting to their preferences

In this paper, we explore two fundamental first-order algorithms in convex optimization, namely, gradient descent (GD) and proximal gradient method (ProxGD). Our focus is on making these algorithms entirely adaptive by leveraging local curvature information of smooth functions.

Table 1: A comparison between sample and oracle complexities (i.e., number of weak learning calls) of the present results and previous works, in each case to achieve

In this work, we define concepts within the machine learning context, highlighting their core properties: expressiveness and model-aware inductive bias, and we make explicit the underlying assumption of CBMs. We establish theoretical results for concept-bottleneck models (CBMs), revealing how these properties guide the design of concept sets that optimize model performance.

The flowchart of our cohort definition protocol is illustrated in Figure 6.

To address these challenges, a multitude of works grounded on these measures have emerged.