Consistent Multilabel Classification
Authors: Oluwasanmi O. Koyejo, Nagarajan Natarajan, Pradeep K. Ravikumar, Inderjit S. Dhillon
NeurIPS 2015 | Conference 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 sanmi@stanford.edu Nagarajan Natarajan Department of Computer Science, University of Texas at Austin naga86@cs.utexas.edu Pradeep Ravikumar Department of Computer Science, University of Texas at Austin pradeepr@cs.utexas.edu Inderjit S. Dhillon Department of Computer Science, University of Texas at Austin inderjit@cs.utexas.edu |
| 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. |