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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Label-Imbalanced and Group-Sensitive Classification under Overparameterization
Authors: Ganesh Ramachandra Kini, Orestis Paraskevas, Samet Oymak, Christos Thrampoulidis
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Importantly, our experiments on state-of-the-art datasets are fully consistent with our theoretical insights and confirm the superior performance of our algorithms. |
| Researcher Affiliation | Academia | Ganesh Ramachandra Kini University of California, Santa Barbara EMAIL Orestis Paraskevas University of California, Santa Barbara EMAIL Samet Oymak University of California, Riverside EMAIL Christos Thrampoulidis University of British Columbia EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | Yes | [1] Code for paper: Label-imbalanced and group-sensitive classification under overparameterization. https://github.com/orparask/VS-Loss. |
| Open Datasets | Yes | Table 1 evaluates LA/CDT/VS-losses on imbalanced instances of CIFAR-10/100... We consider the Waterbirds dataset [45]. |
| Dataset Splits | Yes | For consistency with [17, 8, 32, 54] we keep a balanced test set and in addition to evaluating our models on it, we treat it as our validation set and use it to tune our hyperparameters. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We set an imbalance ratio Nmax/Nmin = 100... For consistency, we follow the training setting in [8]... We use a grid to pick the best τ / γ / (τ,γ)-pair for the LA / CDT / VS losses... In Fig. 4(c1,c3) we trained for 200 epochs, while in Fig. 4(c2,c4) we trained for 300 epochs... For γ = 0.15... We did not fine-tune γ as the heuristic choice already shows the benefit of Group-VS-loss. |