Perfect Sampling from Pairwise Comparisons
Authors: Dimitris Fotakis, Alkis Kalavasis, Christos Tzamos
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work provided a theoretical understanding to perfect sampling from pairwise comparisons. We believe it is an interesting direction to experimentally evaluate our proposed methodology. |
| Researcher Affiliation | Academia | Dimitris Fotakis NTUA fotakis@cs.ntua.gr Alkis Kalavasis NTUA kalavasisalkis@mail.ntua.gr Christos Tzamos UW Madison tzamos@wisc.edu |
| Pseudocode | Yes | Algorithm 1 Exact Sampling from Local Sampling Schemes of Informal Theorem 1 & Theorem 2 |
| Open Source Code | No | The paper explicitly states: 'This work is purely theoretical and has no negative societal impact.' It does not mention any open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use empirical datasets. It refers to a 'Local Sampling Scheme Samp(Q; D)' as a theoretical sample oracle for its model, not an empirical dataset for training. |
| Dataset Splits | No | The paper is purely theoretical and does not describe any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is purely theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is purely theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not describe an experimental setup, hyperparameters, or system-level training settings. |