## Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property
Boshi Wang, Jialin Yi, Hang Dong, Bo Qiao, Chuan Luo, Qingwei Lin
Keywords: 
ICLR/2022/Proceedings/6648 - Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property.pdf
Project URL: nan

### Implementation
_Given the documentation shared by the authors on a new method, how much effort would it be to re-implement the method from scratch?_

[10]

The authors provide a link to their implementation in the reproducibility statement (https://github.com/Microsoft/AutoPredOptConnector). The link gives a 404 error. Brierf overview of the method in figure 1. 

### Data
_Given the data description in the documentation, how much effort would it take to either: Find the same data set the authors used, or a similar data set and defend the comparability, or acquire one from scratch?_

[4]

(2/3)

The authors use Energy Cost-Aware Scheduling, Quandl WIKI dataset and a shortest path dataset. Citations are provided for the latter two are provided, descriptions for all. It is not specified whether the first is public or not.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for obtaining the reported results, and compare them against their computation budget?_

[4]

The authors summarise the configuration in text (5.2) but no structured overview is given nor how they were acquired.

### Experimental Procedure
_Given the setup of experiments reported in the work, how difficult is it to set up a new experiment with the same procedure, similar to those presented in the original work?_

[1]

Results are single runs and metrics are loss function values and problem scores.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying solely on the available documentation?_

[6]

-
