Robust Testing and Estimation under Manipulation Attacks
Authors: Jayadev Acharya, Ziteng Sun, Huanyu Zhang
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study robust testing and estimation of discrete distributions in the strong contamination model... We provide optimal error bounds for both learning and testing. Our lower bounds under local information constraints build on the recent lower bound methods in distributed inference. In the communication constrained setting, we develop novel algorithms based on random hashing and an ℓ1/ℓ1 isometry. |
| Researcher Affiliation | Academia | 1Electrical and Computer Engineering, Cornell University, Ithaca, USA. Correspondence to: Jayadev Acharya <acharya@cornell.edu>, Ziteng Sun <zs335@cornell.edu>, Huanyu Zhang <hz388@cornell.edu>. |
| Pseudocode | No | The paper describes algorithms (e.g., 'Our upper bound proceeds in two stages...', 'We now present an algorithm for γ-robust uniformity testing...') but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public release of source code for the described methodology. It is a theoretical paper. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments requiring dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not discuss experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, hyperparameters, or training settings. |