In recent years, concept-based approaches have emerged as some of the most promising explainability methods to help us interpret the decisions of Artificial Neural Networks (ANNs).

W e explore prediction as an alternative to learning.

W e explore prediction as an alternative to learning. A predictor answers a stream of classification queries instead of outputting a hypothesis.

Gaussian mixtures are a popular model in high-dimensional statistics since, besides being an universal approximator, they often lead to mathematically tractable problems.

We create GTA (a benchmark forGeneral Tool Agents) to evaluate the general tool-use ability ofLLMs inreal-worldscenarios. Who created the dataset (e.g., which team, research group) and on behalf of which entity(e.g.,company,institution,organization)?

We study the problem of exploration in Reinforcement Learning and present a novel model-free solution. We adopt an information-theoretical viewpoint and start from the instance-specific lower bound ofthe number ofsamples that have to be collected to identify a nearly-optimal policy.