A method to convert a Tree Ensemble model into a Rule list. This makes the AI model more transparent.

**MixConv**, or **Mixed Depthwise Convolution**, is a type of [depthwise convolution](https://paperswithcode.com/method/depthwise-convolution) that naturally mixes up multiple kernel sizes in a single [convolution](https://paperswithcode.com/method/convolution). It is based on the insight that depthwise convolution applies a single kernel size to all channels, which MixConv overcomes by combining the benefits of multiple kernel sizes. It does this by partitioning channels into groups and applying a different kernel size to each group.

MixConv

MixConv: Mixed Depthwise Convolutional Kernels

TE2Rules

TE2Rules: Explaining Tree Ensembles using Rules

**Meta Reward Learning (MeRL)** is a meta-learning method for the problem of learning from sparse and underspecified rewards. For example, an agent receives a complex input, such as a natural language instruction, and needs to generate a complex response, such as an action sequence, while only receiving binary success-failure feedback. The key insight of MeRL in dealing with underspecified rewards is that spurious trajectories and programs that achieve accidental success are detrimental to the agent's generalization performance. For example, an agent might be able to solve a specific instance of the maze problem above. However, if it learns to perform spurious actions during training, it is likely to fail when provided with unseen instructions. To mitigate this issue, MeRL optimizes a more refined auxiliary reward function, which can differentiate between accidental and purposeful success based on features of action trajectories. The auxiliary reward is optimized by maximizing the trained agent's performance on a hold-out validation set via meta learning.

Source	TE2Rules: Explaining Tree Ensembles using Rules
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com