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..
Accelerating Stochastic Composition Optimization
Authors: Mengdi Wang, Ji Liu, Ethan X. Fang
JMLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the application of ASC-PG to reinforcement learning and conduct numerical experiments. Section 4 describes an application of ASC-PG to reinforcement learning and gives numerical experiments. Figures 1, 2, and 3 show empirical convergence rates. |
| Researcher Affiliation | Academia | Mengdi Wang: Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544, USA; Ji Liu: Department of Computer Science and Department of Electrical and Computer Engineering University of Rochester Rochester, NY 14627, USA; Ethan X. Fang: Department of Statistics and Department of Industrial and Manufacturing Engineering Pennsyvania State University University Park, PA 16802, USA. All listed institutions are universities. |
| Pseudocode | Yes | Algorithm 1 Accelerated Stochastic Compositional Proximal Gradient (ASC-PG) |
| Open Source Code | No | The paper does not provide any specific statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses "Baird s example (Baird, 1995)" and "generate a Markov decision problem (MDP) using similar setup as in White and White (2016)". Baird's example is a well-known benchmark, but not a dataset with direct access information. For the MDP, the paper states, "In each instance, we randomly generate an MDP which contains S = 100 states..." indicating data generation rather than the use of a pre-existing, openly accessible dataset with explicit access details. |
| Dataset Splits | No | The paper describes generating MDP instances randomly for experiments 2 and 3, and using a known example (Baird's example) for experiment 1. It does not mention any explicit training/test/validation splits for any dataset, as the data is either generated or part of a small illustrative example. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies, libraries, or solvers with their version numbers. |
| Experiment Setup | Yes | We choose the step sizes via comparison studies as in Dann et al. (2014):... for ASC-PG αk = k 1 and βk = k 1, and for a-SCGD αk = k 1 and βk = k 4/5. In the third experiment, we add an ℓ1-regularization term, λ w 1. Figure 3's legend shows 'lambda = 1e-3 lambda = 5e-4'. |