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.
Accelerated Method for Stochastic Composition Optimization With Nonsmooth Regularization
Authors: Zhouyuan Huo, Bin Gu, Ji Liu, Heng Huang
AAAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 15,849 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,802 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results verify our theoretical analysis. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA 2Department of Computer Science, University of Rochester, Rochester, NY, 14627, USA |
| Pseudocode | Yes | Algorithm 3 VRSC-PG |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is publicly available. |
| Open Datasets | No | The paper uses synthetic data generated through Gaussian distributions for portfolio management and generates a Markov decision process (MDP) for reinforcement learning, without providing concrete access information for a publicly available dataset. |
| Dataset Splits | No | The paper uses generated synthetic data but does not explicitly specify training, validation, or testing dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (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 (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiment, we set λ = 10 3 and A = B = b1 = 5. We just select the values of A, B, b1 casually, it is probable that we can get better results as long as we tune them carefully. ... learning rate η is tuned from {1, 10 1, 10 2, 10 3, 10 4}. We keep the learning rate constant for com-SVR-ADMM and VRSC-PG in the optimization. |