Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Modelling Class Noise with Symmetric and Asymmetric Distributions
Authors: Jun Du, Zhihua Cai
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical study shows that, the proposed asymmetric models overall outperform the benchmark linear models, and the asymmetric Laplace-noise model achieves the best performance among all. |
| Researcher Affiliation | Academia | Jun Du School of Computer Science China University of Geosciences Wuhan, P. R. China, 430074 EMAIL Zhihua Cai School of Computer Science China University of Geosciences Wuhan, P. R. China, 430074 EMAIL |
| Pseudocode | Yes | Algorithm 1 Learning asymmetric models |
| Open Source Code | No | The paper states: 'The raw annotation data is provided at https : //sites.google.com/site/nlpannotations/.' This link is for data used in assumption verification, not for the authors' source code. |
| Open Datasets | Yes | Empirical study is conducted on synthetic data and real-world UCI (Bache and Lichman 2013) data. |
| Dataset Splits | Yes | Grid-search on regularization coefficients using 10-fold cross-validation is applied (Hsu, Chang, and Lin 2010). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper references libraries like 'LIBLINEAR' and 'scikit-learn package' and mentions Python, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | More specifically, we assume a zero mean isotropic Gaussian prior on w: p(w) = N(w|0, α^-1I), where α is the precision parameter, and I is the identity matrix. We also assume a Gamma prior (with shape parameter α and scale parameter β ) on λ: p(λ) = Gamma(α , β ). We further set the mode of the Gamma prior to 1, such that the symmetric class-noise models (where λ = 1) are preferred: α-1/β = 1 => α = β + 1. The prior on λ therefore can be formulated: p(λ) = Gamma(β + 1, β) where β > 0. For all the asymmetric models, we initialize λ to 1, and w to the final solutions of the symmetric counterparts. |