To achieve effective zero-shot semantic segmentation (ZSS), existing efforts have been made [2, 28,31,44,55,62]bytreating eachpixelasanindependent classification problem.

In the bottom row of Figure 1 we consider tight weight eigenvalue constraints based on Greshgorin discs.

Decision trees are well-known due to their ease of interpretability. To improve accuracy, we need to grow deep trees or ensembles of trees. These are hard to interpret, offsetting their original benefits. Shapley values have recently become a popular way to explain the predictions of tree-based machine learning models. It provides a linear weighting to features independent of the tree structure. The rise in popularity is mainly due to TreeShap, which solves a general exponential complexity problem in polynomial time. Following extensive adoption in the industry, more efficient algorithms are required. This paper presents a more efficient and straightforward algorithm: Linear TreeShap. Like TreeShap, Linear TreeShap is exact and requires the same amount of memory.

In ICL, a conditional generative model (CGM) is prompted with a dataset and a prediction question and asked to generate a response.

The task is to i) predict the unknown parameters, then ii) solve the optimization problem using the predicted parameters, such that the resulting solutions are good even under true parameters.

The task is to i) predict the unknown parameters, then ii) solve the optimization problem using the predicted parameters, such that the resulting solutions are good even under true parameters.

Value function factorization (VFF) is a popular approach to cooperative multiagent reinforcement learning in order to learn local value functions from global rewards.