## Any-scale Balanced Samplers for Discrete Space
Haoran Sun, Bo Dai, Charles Sutton, Dale Schuurmans, Hanjun Dai
Keywords: 
ICLR/2023/Proceedings/11313 - Any-scale Balanced Samplers for Discrete Space.pdf
Project URL: nan

### Implementation
_Given the documentation shared by the authors on a new method, how much effort would it be to re-implement the method from scratch?_

[9]

The authors do not provide their implementation. Pseudo code avaialble in algorithm 1-4.

### Data
_Given the data description in the documentation, how much effort would it take to either: Find the same data set the authors used, or a similar data set and defend the comparability, or acquire one from scratch?_

[5]

(5/5)

The authors conduct experiments on two types of synthetic data (functions), and describe them in 5.2 but do not provide the code or data. Furthermore they use MNIST, Omniglot, and Caltech datasets. No citations or descriptions.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for obtaining the reported results, and compare them against their computation budget?_

[4]

The authors provide default values for their algorithms in the pseudo code, although it would have to be verified whether these are the actual values used in each experiment. No motivation given for the initial values.

### Experimental Procedure
_Given the setup of experiments reported in the work, how difficult is it to set up a new experiment with the same procedure, similar to those presented in the original work?_

[1]

The authors describe the metrics in 5.1. Results on datasets are training curves.

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

[6]

-
