Learning from Corrupted Binary Labels via Class-Probability Estimation
Authors: Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, Bob Williamson
ICML 2015 | Conference 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 ADITYA.MENON@NICTA.COM.AU Brendan van Rooyen BRENDAN.VANROOYEN@NICTA.COM.AU Cheng Soon Ong CHENGSOON.ONG@NICTA.COM.AU Robert C. Williamson BOB.WILLIAMSON@NICTA.COM.AU 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. |