Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Learning and Covering Sums of Independent Random Variables with Unbounded Support
Authors: Alkis Kalavasis, Konstantinos Stavropoulos, Emmanouil Zampetakis
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of covering and learning sums X= X1 + + Xn of independent integer-valued random variables Xi(SIIRVs) with in๏ฌnite support. Our results are of theoretical nature and we do not identify any direct potential negative societal impact. |
| Researcher Affiliation | Academia | Alkis Kalavasis National Technical University of Athens EMAIL Konstantinos Stavropoulos The University of Texas at Austin EMAIL Manolis Zampetakis University of California, Berkeley EMAIL |
| Pseudocode | Yes | Figure 1: Algorithm for Learning SIIURVs |
| Open Source Code | No | The paper does not contain any statements about releasing code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention the use of any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not use datasets or specify any training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail any experimental setup, hyperparameters, or training configurations. |