
## Contraction rates for sparse variational approximations in Gaussian process regression
Dennis Nieman, Botond Szabo, Harry van Zanten
Keywords: 
JMLR/2022/Proceedings/211128 - Contraction rates for sparse variational approximations in Gaussian process regression.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]

Implementation not provided.

### 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?_

[3]

(1/1)

The authors use two formulas for the synthetic experiment and provide the variable and its value, show it on figure 1 and 4. Although the implementation is not given, it looks fairly straightforward to do.

### 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]

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 repeated the experiment 100 times and present the average + std deviation in table 1. Metric is KL-divergence.

### 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 variational bayes.
