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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |