
## Joint Continuous and Discrete Model Selection via Submodularity
Jonathan Bunton, Paulo Tabuada
Keywords: 
JMLR/2022/Proceedings/210166 - Joint Continuous and Discrete Model Selection via Submodularity.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?_

[8]

The authors state the usage of IBM CPLEx 12.8 in MATLAB and cite another matlab toolbox they use. Implementation not given.

### 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]

(4/4)

The authors use simulated problems and define all of them in detail with parameters and values in section 8. In 8.3 they use a dataset from the UCI repository (citations provided, details can be better) to construct the problem. 

### 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?_

[10]

The authors state various parameter values but its not clear what the parameters are (if any) for the algorithms.

### 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]

The authors evaluate the methods in single runs over n iterations and measure the objective function and running time is seconds. No data split.

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

[9]

Requries expertise on mixed continuous discrete optimization.
