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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Learning a Mixture of Two Multinomial Logits
Authors: Flavio Chierichetti, Ravi Kumar, Andrew Tomkins
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 19,073 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,691 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we give the first polynomial-time algorithms for exact learning of uniform mixtures of two MNLs. Interestingly, the parameters of the model can be learned for any n by sampling the behavior of random users only on slates of sizes 2 and 3; in contrast, we show that slates of size 2 are insufficient by themselves. |
| Researcher Affiliation | Collaboration | 1Sapienza University, Rome, Italy 2Google, Mountain View, CA. |
| Pseudocode | No | The paper describes algorithms in text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and proofs; it does not describe experiments involving specific datasets, training, or public access information for them. |
| Dataset Splits | No | The paper is theoretical and focuses on algorithm design and proofs; it does not describe experiments involving specific dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe the specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |