Low experiment cost, Domain-specific problem structure, Large quantity of similar operators.

Polyhedral models are a popular choice for S_e (search space); they model the loop domains as integer linear constrains. An alternative approach originating from halide defines a schedule space using a set of transformation primitives. –> This paper **uses one in TVM to form S_e**!

#### Learning to Optimize Tensor Programs

They use GBTs (XGBoost) and **TreeGRU**.

Training objective function. Common choice is the regression which encourages the model to predict cost accurately. They instead use **rank loss function**.

Use **Simulated Annealing with the loss function as the energy function to explore**. We select the top-performing batch of candidates to run on real hardware. **Diversity-Aware Exploration** is achieved by maximizing the objective to select candidate set S from top candidates (3). **Uncertainty Estimator**… I don’t know

#### Accelerating Optimization via Transfer Learning

In practice want to optimize for many tensor operators with different input shapes and data types. Apply transfer learning… Key is to create a **transferable representation** that is** invariant** to the source and target domains.

Common practice is to directly use configuration as the model’s input. However, search space specification can change for different workloads or when the user specifies a new search space for the same workload. The configuration representation is not invariant to changes in the search space.

Low-level loop AST is a shared representation of programs that is invariant to the search space. To leverage invariance, use low-level loop AST as input.