Learning a Mixture of Two Multinomial Logits
Authors: Flavio Chierichetti, Ravi Kumar, Andrew Tomkins
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |