
## Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes
Marin Ballu, Quentin Berthet
Keywords: 
ICML/2023/Proceedings/24396 - Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes.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. The authors provide pseudo code in algorithm 1 and 3.

### 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?_

[6]

(2/3)

The authors evaluate the method on synthetic data for which they define the parameters but do not provide the implementation and on two public data sets. They do not provide any details on these datasets.

### 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]

There are a few defined parameters on their method in algorithm 1/3 but these are seemingly replaced with formulas later ('defined'). Thus their method seems to be parameter free.

### 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 define the objective per problem. They repeat each experiment for 32 runs with median and 10th-90th percentile. Data split not applicable.

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

[6]

Requires expertise on online optimization and sinkhorn.
