Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Constrained Binary Decision Making
Authors: Daniel Průša, Vojtech Franc
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 24,122 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,787 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present a comprehensive formulation of the BDM problem and thoroughly characterize the optimal strategy. Our framework encompasses various BDM problems as special cases, enabling us to derive optimal decision strategies for these instances. This provides a robust mathematical tool for solving both existing and new BDM problems. The related theorem is highly general, applying to both discrete and continuous instance spaces without requiring the differentiability of decision and loss functions, unlike common proof techniques based on Lagrange duality. |
| Researcher Affiliation | Academia | Daniel Pr uša Vojtˇech Franc Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague EMAIL |
| Pseudocode | No | The paper presents mathematical forms of optimal strategies (e.g., equations 5, 9, 13, 15) and describes steps for deriving solutions, but it does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any open-source code for the methodology described. |
| Open Datasets | No | The paper does not include any experiments. |
| Dataset Splits | No | The paper does not include any experiments. |
| Hardware Specification | No | The paper does not include any experiments. |
| Software Dependencies | No | The paper does not include any experiments. |
| Experiment Setup | No | The paper does not include any experiments. |