Compressed Maximum Likelihood
Authors: Yi Hao, Alon Orlitsky
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper concerns functional estimation, where P is a distribution collection over a domain space Z, and f : P Q is a functional, mapping distributions in P into a space Q equipped with a pseudo-metric d. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, University of California, San Diego, USA. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper refers to drawing 'samples' from distributions for theoretical analysis, but it does not specify any publicly available datasets used for training or empirical evaluation. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., train/validation/test percentages or counts) as it is primarily theoretical. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameters, training configurations, or system-level settings, as it is a theoretical work. |