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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |