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..
Optimal-er Auctions through Attention
Authors: Dmitry Ivanov, Iskander Safiulin, Igor Filippov, Ksenia Balabaeva
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate both modifications in an extensive experimental study that includes settings with constant and inconstant numbers of items and participants, as well as novel validation procedures tailored to regret-based approaches. |
| Researcher Affiliation | Collaboration | Dmitry Ivanov HSE University & Technion Israel Iskander Safiulin Independent researcher Russia Igor Filippov Independent researcher Russia Ksenia Balabaeva ITMO University & BIOCAD Russia |
| Pseudocode | No | The paper includes a figure illustrating the architecture (Figure 1) and describes its components verbally in Section 3.1, but it does not provide structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Code is available here |
| Open Datasets | No | The paper describes how the data is generated ( |
| Dataset Splits | No | The paper states that |
| Hardware Specification | No | The paper states that research was supported |
| Software Dependencies | No | The paper mentions deep learning frameworks and concepts (e.g., |
| Experiment Setup | No | The paper states that |