
## Three rates of convergence or separation via U-statistics in a dependent framework
Quentin Duchemin, Yohann De Castro, Claire Lacour
Keywords: 
JMLR/2022/Proceedings/210819 - Three rates of convergence or separation via U-statistics in a dependent framework.pdf
Project URL: https://github.com/quentin-duchemin/goodness-of-fit-MC

### 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]

The authors provide a link to their implementation on JMLR and in footnote 1 of 5.3 (https://github.com/quentin-duchemin/goodness-of-fit-MC). There are no installation notes. The notebook could use more text / comments.

### 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?_

[1]

(1/1)

The authors use simulated data and specify the parameters. Code is available.

### 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]

Not 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?_

[1]

The authors evaluate with three tests that they define over various my and sigma squared that they define before each example. 

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

[7]

Requires expertise on markov chains and U-statistics.
