Learning from Corrupted Binary Labels via Class-Probability Estimation
Authors: Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, Bob Williamson
ICML 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on label noise tasks corroborate our analysis.We now present experiments that aim to validate our analysis4 via three questions. |
| Researcher Affiliation | Collaboration | Aditya Krishna Menon EMAIL Brendan van Rooyen EMAIL Cheng Soon Ong EMAIL Robert C. Williamson EMAIL National ICT Australia and The Australian National University, Canberra The Australian National University and National ICT Australia, Canberra |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Sample scripts are available at http://users.cecs.anu.edu. au/ akmenon/papers/corrupted-labels/index.html. |
| Open Datasets | Yes | We report results on a range of UCI datasets. |
| Dataset Splits | Yes | For each dataset, we construct a random 80% 20% train-test split.The regularisation parameter for the model was tuned by cross-validation (on the corrupted data) based on squared error. |
| Hardware Specification | No | No specific details about the hardware (e.g., GPU/CPU models, memory, or cloud computing resources) used for running the experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions training a neural network with ℓ2 regularization, but does not provide specific version numbers for any software dependencies or libraries used (e.g., Python, TensorFlow, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | We focus on CCN learning with label flip probabilities ρ+, ρ {0, 0.1, 0.2, 0.3, 0.4, 0.49};... we use as our base model a neural network with a sigmoidal hidden layer, trained to minimise squared error5 with ℓ2 regularisation. The regularisation parameter for the model was tuned by cross-validation (on the corrupted data) based on squared error. |