Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
Authors: Shafi Goldwasser, Adam Tauman Kalai, Yael Kalai, Omar Montasser
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments As a proof of concept, we perform simple controlled experiments on the task of handwritten letter classification using lower-case English letters from the EMNIST dataset (Cohen et al. [2017]). |
| Researcher Affiliation | Collaboration | ShafiGoldwasser UC Berkeley, MIT Adam Tauman Kalai Microsoft Research Yael Tauman Kalai Microsoft Research, MIT Omar Montasser TTI Chicago |
| Pseudocode | Yes | Figure 2: The 햱햾헃햾햼헍헋허헇 algorithm takes labeled training examples and unlabeled test examples as input, and it outputs a selective classifier ℎ|푆that predicts ℎ(푥) for 푥 푆(and rejects all 푥 푆). |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | EMNIST dataset (Cohen et al. [2017]) |
| Dataset Splits | No | The paper mentions using the EMNIST dataset for experiments and describes two experimental setups, but it does not provide specific train/validation/test split percentages, sample counts, or explicit references to predefined splits for reproducibility. |
| Hardware Specification | No | The paper describes performing 'simple controlled experiments' but does not provide any specific hardware details such as GPU or CPU models, or memory specifications used for running these experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software libraries or dependencies used in the experiments. |
| Experiment Setup | No | The paper outlines two experimental setups for the handwritten letter classification task but does not provide specific details on hyperparameters, training configurations, or other system-level settings required to reproduce the experiments. |