
## No Internal Regret with Non-convex Loss Functions
Dravyansh Sharma
Keywords: ML: Online Learning & Bandits, SO: Algorithm Configuration
AAAI/2024/Proceedings/30665 - No Internal Regret with Non-convex Loss Functions.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 author documents the method with pseudo code sections and clear mathematical notations and proofs. However no implementation is given as it is a theoretical paper.

### 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)

The author outlines two problems their method is designed to resolve, and shows with examples but presents no data-based results.

### 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]

No algorithm configurations were used.

### 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 experimental procedure was used.

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

[6]

The theorems presented in this paper are highly complex, and can take the independent investigator many reads to fully grasp before they will be able to reproduce or apply them. The author is very clear in their definitions, and presents many relevant citations as support.
