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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Unified Lower Bounds for Interactive High-dimensional Estimation under Information Constraints
Authors: Jayadev Acharya, Clément L Canonne, Ziteng Sun, Himanshu Tyagi
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main focus is on information-theoretic lower bounds for the minimax error rates (or, equivalently, the sample complexity) of these problems. and We provide a unified framework enabling us to derive a variety of (tight) minimax lower bounds for different parametric families of distributions, both continuous and discrete, under any ℓp loss. |
| Researcher Affiliation | Collaboration | Jayadev Acharya Cornell University EMAIL Clément L. Canonne University of Sydney EMAIL Ziteng Sun Google Research, New York EMAIL Himanshu Tyagi Indian Institute of Science, Bangalore EMAIL |
| Pseudocode | Yes | Algorithm 1 LDP protocol for mean estimation for the product of Bernoulli family and Algorithm 2 ℓ-bit protocol for estimating product of Bernoulli family |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus it does not mention public datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, therefore it does not discuss training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments or the hardware used to perform them. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithms, not specific software implementations. Therefore, no software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical frameworks and algorithms, not practical experimental setups. Therefore, no specific details like hyperparameters or system-level training settings are provided. |