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..
Surveys without Questions: A Reinforcement Learning Approach
Authors: Atanu R Sinha, Deepali Jain, Nikhil Sheoran, Sopan Khosla, Reshmi Sasidharan257-264
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On validation against actual survey data, proxy ratings yield reasonable performance results. The dataset is split randomly into two groups for training (75%) and testing (25%). |
| Researcher Affiliation | Industry | 1Adobe Research, India 2Adobe, India EMAIL, EMAIL,EMAIL |
| Pseudocode | No | The paper describes the methods using mathematical equations and text, and includes a diagram (Figure 1), but does not present structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository. |
| Open Datasets | No | The paper states, "Clickstream data from the website of a consumer electronics company are used," indicating proprietary data without providing any public access information, links, or citations for the dataset. |
| Dataset Splits | Yes | The dataset is split randomly into two groups for training (75%) and testing (25%). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using an LSTM model and RNN, but does not provide specific software names with version numbers for dependencies (e.g., 'TensorFlow' or 'PyTorch' with versions). |
| Experiment Setup | No | The paper mentions a fixed dimension of 150 chosen based on limited hyper-parameter tuning (50, 100, 150, 200) for state representation, and refers to a 'learning rate' (alpha) but does not provide specific values for these or other hyperparameters like batch size, epochs, or optimizer settings. |