
## Scalable Optimal Margin Distribution Machine
Yilin Wang, Nan Cao, Teng Zhang, Xuanhua Shi, Hai Jin
Keywords: Machine Learning: ML: Classification, Data Mining: DM: Big data and scalability, Machine Learning: ML: Kernel methods
IJCAI/2023/Proceedings/0485 - Scalable Optimal Margin Distribution Machine.pdf

### 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?_

[3]

[Code available online.
Spark cluster required for installation +1
Cryptic comments +1
directory able to navigate +0]

### 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?_

[10]

(0/8)

[Only results on named datasets given +10]

### 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?_

[9]

[hyperparameter space table is missing +3 and its not clear where to find HPS
Values are missing because space is missing +4
Not stated a strategy + 1]

### 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?_

[4]

[Metrics are clear +0
dataset is split randomly but missing seed +2
implies holdout +1
data is clearly aggregated +0]

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

[8]

[i think the method is quite clearly documented but execution will be hard on spark, also reprodcution of results impossible if datasets are private.]
