Towards Black-box Iterative Machine Teaching
Authors: Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James Rehg, Le Song
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we compare the proposed active teacher with the omniscient teacher and verify the effectiveness of the active teacher model. |
| Researcher Affiliation | Collaboration | 1Georgia Tech 2University of Minnesota 3Ant Financial. |
| Pseudocode | Yes | Algorithm 1 The active teacher |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We apply the active teacher to teach the LR learner on the MNIST dataset (Le Cun et al., 1998) |
| Dataset Splits | No | The paper uses terms like "training set" and "testing set" but does not provide specific percentages, sample counts, citations to predefined splits, or detailed splitting methodology for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiments. |
| Experiment Setup | No | The paper mentions general settings and implementation details in Appendix B, such as "We implement all the models by Python and conduct experiments using CPU" and how data is generated, but it does not specify concrete hyperparameter values like learning rates, batch sizes, or optimizer settings for the experiments. |