
## Least Squares Model Averaging for Distributed Data
Haili Zhang, Zhaobo Liu, Guohua Zou
Keywords: 
JMLR/2023/Proceedings/220511 - Least Squares Model Averaging for Distributed Data.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?_

[10]

The authors do not provide their implementation.

### 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)

In section 4 the authors conduct experiments on simulated data and elaborate on the process and parameters extensively. No implementation given. In section 5 they use real world data for which they state the ASA Data Expo with a link and discuss it at length. Statistics are minor. 

### 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 method does, asside from a draw repition/simulation replication parameter (given) not any parameters it seems.

### 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?_

[1]

Metric is mean squared prediction errors and risk defined in section 3. Data split and variation seem not to be applicable.

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

[7]

Requries expertise on model averaging and distributed data.
