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..
A Composite Randomized Incremental Gradient Method
Authors: Junyu Zhang, Lin Xiao
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide numerical experiments in Section 5. In this part, we present numerical experiments for two applications: risk-averse portfolio optimization and policy evaluation for Markov decision processes. Figure 1. Experiments on the risk-averse portfolio optimization problem. Figure 2. Experiments on MDP policy evaluation problem. |
| Researcher Affiliation | Collaboration | 1Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota, USA. 2Microsoft Research, Redmond, Washington, USA. |
| Pseudocode | Yes | Algorithm 1 Composite SAGA (C-SAGA) |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the source code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | No | In our experiments, the reward vectors Ri are first generated as n i.i.d Gaussian random vectors with a random correlation matrix C = LLT, where L Rd d satisfies N(0, 1) distribution elementwise. In the experiments, Pπ, Φ and Rπ are generated randomly. |
| Dataset Splits | No | The paper uses randomly generated data for its experiments and does not provide specific dataset split information (percentages, sample counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Both VRSC-PG and C-SAGA use the same step size η = 0.001 and batch size s = n2/3 . They are chosen from by experimenting with {1, 0.1, 0.01, 0.001, 0.0001}, and η = 0.001 works best for VRSC-PG and for C-SAGA. For the case where S = 10, both VRSC-PG and C-SAGA use the same batch size s = 1. C-SAGA takes a step size η = 0.1, while VRSC-PG takes a stepsize of η = 0.03, because it diverges under η = 0.1 and η = 0.03 seems to work best VRSC-PG. For S = 100, we set η = 0.005 and batch size s = 10 for C-SAGA and VRSC-PG. The step size is chosen as the best among {0.1, 0.05, 0.01, 0.005, 0.001}. |