Adversarially Robust PAC Learnability of Real-Valued Functions
Authors: Idan Attias, Steve Hanneke
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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. |