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..
Adversarially Robust PAC Learnability of Real-Valued Functions
Authors: Idan Attias, Steve Hanneke
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | From the theoretical standpoint, there has been a lot of effort to provide provable guarantees of such methods (e.g., Feige et al. (2015); Schmidt et al. (2018); Khim & Loh (2018); Yin et al. (2019); Cullina et al. (2018); Attias et al. (2019; 2022); Montasser et al. (2021b; 2020a;b; 2021a); Ashtiani et al. (2020); Dan et al. (2020); Awasthi et al. (2020; 2021b;a; 2022a;b; 2023); Bhattacharjee et al. (2021); Xing et al. (2021); Mao et al. (2023)), which is the focus of this work. Our main technique is based on a construction of an adversarially robust sample compression scheme of a size determined by the fat-shattering dimension. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Ben-Gurion University, Israel 2Department of Computer Science, Purdue University, USA. |
| Pseudocode | Yes | Algorithm 1 Improper Robust Regressor with High-Vote, Algorithm 2 Improper Robust Regressor, Algorithm 3 Improper Robust (η, β)-Regressor, Algorithm 4 Modified Multiplicative Weights, Algorithm 5 Modified Med Boost, Algorithm 6 Sparsify, Algorithm 7 |
| Open Source Code | No | The paper does not contain any statement about making its source code available or provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not mention specific public datasets, nor does it provide links or citations for data access. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental data splits (training, validation, or test sets). |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe concrete experimental setup details, hyperparameters, or training configurations. |