
## H-Consistency Guarantees for Regression
Anqi Mao, Mehryar Mohri, Yutao Zhong
Keywords: 
ICML/2024/Proceedings/33103 - H-Consistency Guarantees for Regression.pdf
Project URL: nan

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[9]

The implementation is not shared. The authors state the library used to optimise their losses in section 6. 

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

[4]

(2/2)

The authors state they studied two real world data sets in section 6. They provide citations on these datasets. No statistics or descriptions given. No direct link found.

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

[1]

The authors method is mainly an analysis. No parameters applicable.

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

[3]

The authors report standard error over MSE on the test data. They average over five runs. The authors do not state where the test data comes from (static? givben by the authors of the data?). 

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

[7]

Requires experience in H consistency, loss functions and optimization. 
