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..
GO Hessian for Expectation-Based Objectives
Authors: Yulai Cong, Miaoyun Zhao, Jianqiao Li, Junya Chen, Lawrence Carin12060-12068
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Leveraging the GO Hessian, we develop a new second-order method for Eqγ (y)[f(y)], with challenging experiments conducted to verify its effectiveness and efficiency. The proposed techniques are verified with challenging experiments where non-rep gamma RVs are of interest. |
| Researcher Affiliation | Academia | Yulai Cong, Miaoyun Zhao,* Jianqiao Li, Junya Chen, Lawrence Carin Department of Electrical and Computer Engineering, Duke University |
| Pseudocode | Yes | Algorithm 1 SCR-GO for minγ Eq(x)qγ(y|x)[f(x, y)] |
| Open Source Code | Yes | Code will be available at github.com/YulaiCong/GOHessian. |
| Open Datasets | Yes | Variational Encoders for PFA and PGBN... Figures 4(b)-4(c) show the training objectives versus the number of oracle calls and processed observations... PFA on MNIST. |
| Dataset Splits | No | The paper describes using training datasets and showing training curves but does not explicitly provide details on train/validation/test splits (e.g., percentages, sample counts, or specific predefined splits). |
| Hardware Specification | Yes | The Titan Xp GPU used was donated by the NVIDIA Corporation. |
| Software Dependencies | No | The paper mentions software like PyTorch and TensorFlow but does not specify their version numbers or any other software dependencies with specific versions. |
| Experiment Setup | Yes | For both SGD and Adam, learning rates from {0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1} are tested with the best-tuned results shown. Other settings are given in Appendix J. |