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..
Efficiently Learning Adversarially Robust Halfspaces with Noise
Authors: Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro
ICML 2020 | Venue PDF | 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. |