
## A General Theoretical Framework for Learning Smallest Interpretable Models
Sebastian Ordyniak, Giacomo Paesani, Mateusz Rychlicki, Stefan Szeider
Keywords: KRR: Computational Complexity of Reasoning, ML: Transparent, Interpretable, Explainable ML
AAAI/2024/Proceedings/29781 - A General Theoretical Framework for Learning Smallest Interpretable Models.pdf

[X] Theoretical

### 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?_

[1]

The authors provide a theoretical framework for s learning the smallest symbolic models that represent given data. The authors do not do any empirical evaluation, and therefore implementation is not applicable.

### 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]

(0/0)

It is a theoretical paper with out empircial evaluations on data sets.

### 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]

Algorithm configuration *values* do not apply in this case.

### 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]

No empirical evaluation was done.

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

[8]

As it is a theoretical paper, the expertise required to reproduce their defitions, stated observations, including their proofs, will require a deep understanding of mathematical theory.