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..
Hedging as Reward Augmentation in Probabilistic Graphical Models
Authors: Debarun Bhattacharjya, Radu Marinescu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the concepts with examples and counter-examples, and conduct experiments to demonstrate the properties and applicability of the proposed computational tools that enable agents to proactively identify potential hedging opportunities in real-world situations. |
| Researcher Affiliation | Industry | Debarun Bhattacharjya IBM Research EMAIL Radu Marinescu IBM Research EMAIL |
| Pseudocode | No | The paper describes methods in text and equations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] All the information necessary to conduct the experiments is provided in the paper itself. |
| Open Datasets | Yes | The numbers are based on real data from 1996 to 1998 (see Appendix C.1). |
| Dataset Splits | No | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] The experiments do not require extensive compute power. |
| Software Dependencies | No | Computations were done using exact Bayesian inference available from the pgmpy2 package (https://github.com/pgmpy/pgmpy). However, no specific version number for pgmpy or any other software is mentioned. |
| Experiment Setup | Yes | Consider the following numerical case: σ = 1, expected performances for tasks: P1 = [2, 1], P2 = [3, 2], P3 = [1, 7]. With these numbers, policy π = h is optimal a-priori. |