On Optimal Learning Under Targeted Data Poisoning
Authors: Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we aim to characterize the smallest achievable error. We fully achieve this in the realizable setting, proving that ε = Ω(VC(H) γ). We then also show a matching lower bound. |
| Researcher Affiliation | Collaboration | Steve Hanneke Purdue University, USA; Amin Karbasi Yale University, USA and Google Research; Mohammad Mahmoody University of Virginia, USA; Idan Mehalel Technion, Israel; Shay Moran Technion, Israel and Google Research |
| Pseudocode | Yes | Figure 1: SPV A meta algorithm implementing a stable version of the input learning algorithm Lrn. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code or a link to a source code repository. The 'Checklist' sections regarding code are marked 'N/A'. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with specific datasets. The checklist explicitly states 'N/A' for data and experimental results. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies with specific datasets, therefore it does not mention training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments, therefore no hardware specifications are mentioned. The 'Checklist' sections regarding compute resources are marked 'N/A'. |
| Software Dependencies | No | The paper is theoretical and does not report on empirical experiments, therefore no specific software dependencies with version numbers are listed. The 'Checklist' sections regarding compute resources are marked 'N/A'. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and algorithm design, not on the experimental setup, hyperparameters, or training details of empirical experiments. The 'Checklist' sections regarding training details are marked 'N/A'. |