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.
Interaction-Grounded Learning
Authors: Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,981 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,119 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 | In this section, we provide empirical evaluations in simulated environments. We experiment with both batch and online IGL. |
| Researcher Affiliation | Collaboration | Tengyang Xie 1 John Langford 2 Paul Mineiro 2 Ida Momennejad 2 1University of Illinois at Urbana-Champaign 2Microsoft Research, New York City. |
| Pseudocode | Yes | Algorithm 1 E2G |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluated our approach on the MNIST environment based on the in๏ฌnite MNIST simulator (Loosli et al., 2007). |
| Dataset Splits | No | The paper states: 'Over our experiments in the batch mode, we use the uniform policy ฯbad to gather data, and the number of examples is 60000.' It does not specify train, validation, or test splits for this dataset. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'logistic regression' and 'softmax policies' but does not specify any software names with version numbers for reproducibility (e.g., specific libraries or frameworks like PyTorch 1.9 or TensorFlow 2.x). |
| Experiment Setup | No | The paper states: 'We provide the details on setting up the experiments in Appendix B', but Appendix B is not included in the provided text, hence no specific experimental setup details are present in the main body. |