Consistent Multilabel Classification
Authors: Oluwasanmi O. Koyejo, Nagarajan Natarajan, Pradeep K. Ravikumar, Inderjit S. Dhillon
NeurIPS 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on synthetic and benchmark datasets are supportive of our theoretical findings. |
| Researcher Affiliation | Academia | Oluwasanmi Koyejo Department of Psychology, Stanford University EMAIL Nagarajan Natarajan Department of Computer Science, University of Texas at Austin EMAIL Pradeep Ravikumar Department of Computer Science, University of Texas at Austin EMAIL Inderjit S. Dhillon Department of Computer Science, University of Texas at Austin EMAIL |
| Pseudocode | Yes | Algorithm 1: Plugin-Estimator for micro and instance |
| Open Source Code | No | The paper does not contain an explicit statement or a link to the authors' own open-source code for the described methodology. |
| Open Datasets | Yes | We use four benchmark multilabel datasets4 in our experiments: (i) SCENE, an image dataset [...] (ii) BIRDS [...] (iii) EMOTIONS [...] and (iv) CAL500 [...]. The datasets were obtained from http://mulan.sourceforge.net/datasets-mlc.html. |
| Dataset Splits | Yes | Then, the given metric micro(f) is maximized on a validation sample. [...] Algorithm 1: Plugin-Estimator for micro and instance [...] 2. Split the training data Sm into two sets Sm1 and Sm2. [...] Obtain ˆδ by solving (12) on S2 = [M m=1Sm2. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models, memory, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions performing 'logistic regression (with L2 regularization)' but does not specify any software names with version numbers (e.g., Python, PyTorch, scikit-learn versions). |
| Experiment Setup | No | The paper mentions using 'logistic regression (with L2 regularization)' and tuning a threshold on a validation set. However, it does not provide specific hyperparameter values for the regularization, learning rate, batch size, or other detailed training configurations. |