Efficiently Learning Adversarially Robust Halfspaces with Noise
Authors: Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The entire paper is focused on theoretical analysis, definitions, lemmas, theorems, and algorithmic descriptions without any experimental section, dataset evaluations, or performance tables. For example, Section 3 "The Realizable Setting" and Section 4 "Random Classification Noise" are purely mathematical analyses. No empirical results are presented. |
| Researcher Affiliation | Academia | 1Toyota Technological Institute at Chicago 2University of Texas at Austin 3University of Wisconsin-Madison. |
| Pseudocode | No | The paper describes algorithmic procedures (e.g., in Lemma 3.7, 4.3) using prose and mathematical equations but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | No | This paper is theoretical and does not describe or run experiments on any specific dataset; thus, there is no mention of a publicly available dataset for training. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with datasets, and therefore no dataset split information for validation is provided. |
| Hardware Specification | No | This paper is theoretical and does not involve running computational experiments; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not discuss implementation details or require specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | This paper is theoretical and does not describe any empirical experiments; therefore, there is no experimental setup information, including hyperparameters or system-level training settings. |