To address this, we propose a new probabilistic framework for DG where the goal is to learn predictors that perform wellwith highprobability.

Decision trees have gained popularity as interpretable surrogate models for learning-based control policies.

Since the VB algorithm does not depend on the unknown truth to achieve optimality, our results shed light on effective prior choices.

Wepropose anend-to-end machine learning based approach for topological ordering using an encoder-decoder framework. Our encoder is a novel attention based graph neural network architecture called Topoformer which uses different topological transforms of a DAG for message passing.

Wepropose anend-to-end machine learning based approach for topological ordering using an encoder-decoder framework. Our encoder is a novel attention based graph neural network architecture called Topoformer which uses different topological transforms of a DAG for message passing.

The Order Oracle has the capability to compare two functions; however, in contrast to the zero-order oracle, it lacks the ability to calculate or utilize the actual value of the objective function. This concept closely mirrors the challenges encountered in real-world black-box optimization problems.

We provide a novel method for lifelong policy gradient learning that trains lifelong function approximators directly via policygradients, allowing the agent to benefit from accumulated knowledge throughout the entire training process.