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.
Communication-Constrained Inference and the Role of Shared Randomness
Authors: Jayadev Acharya, Clement Canonne, Himanshu Tyagi
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 19,082 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,719 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 | We study problems of distribution learning and identity testing in this distributed inference setting and examine the role of shared randomness as a resource. We propose a general purpose simulate-and-infer strategy that uses only private-coin communication protocols and is sample-optimal for distribution learning. This general strategy turns out to be sample-optimal even for distribution testing among private-coin protocols. Interestingly, we propose a public-coin protocol that outperforms simulate-and-infer for distribution testing and is, in fact, sample-optimal. |
| Researcher Affiliation | Academia | 1Cornell University 2Stanford University 3Indian Institute of Science Institute of Technology. Correspondence to: Clément Canonne <EMAIL>. |
| Pseudocode | No | The paper describes protocols and schemes in paragraph form, such as 'We describe the base version of the protocol below...' in Theorem 4.1, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on statistical inference and communication complexity; it does not describe using any datasets for training or provide access information for publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with datasets, thus it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe running experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithm design, not specific software implementations. It does not list any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental procedures or configurations such as hyperparameters or training schedules. |