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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning
Authors: Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, the empirical results on benchmark datasets not only validate the theoretical results but also demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Collaboration | 1 SKLOIS, Institute of Information Engineering, CAS 2 School of Cyber Security, University of Chinese Academy of Sciences 3 Key Lab. of Intelligent Information Processing, Institute of Computing Tech., CAS 4 School of Computer Science and Tech., University of Chinese Academy of Sciences 5 Alibaba Group 6 School of Cyber Science and Tech., Shenzhen Campus of Sun Yat-sen University 7 BDKM, University of Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1: Principled Learning Algorithm induced by the Theoretical Insights |
| Open Source Code | No | The paper does not provide a link to its source code or explicitly state its release. |
| Open Datasets | Yes | Datasets. We conduct the experiments on four popular benchmark datasets for imbalanced learning. (a) CIFAR-10 and CIFAR-100: Following the protocol in [26, 11, 12]... (b) Image Net-LT and i Naturalist: We use the long-tailed version of the Image Net dataset2 [2] proposed by [27], and i Naturalist3 [5] is a real-world long-tailed dataset. |
| Dataset Splits | Yes | Datasets. We conduct the experiments on four popular benchmark datasets for imbalanced learning. (a) CIFAR-10 and CIFAR-100: Following the protocol in [26, 11, 12]... (b) Image Net-LT and i Naturalist: We use the long-tailed version of the Image Net dataset2 [2] proposed by [27], and i Naturalist3 [5] is a real-world long-tailed dataset. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Pytorch' [51], 'numpy' [52], 'scikit-learn' [53], and 'Torchvision' [54] but does not provide specific version numbers for these software dependencies as used in their experiments. |
| Experiment Setup | Yes | We tune the hyperparameter ν, and the other hyperparameters follow those used in the baselines. In addition, we incorporate the Sharpness-Aware Minimization (SAM) technique [28]... Algorithm 1 details: if t < T0 then Set α = 1, βy, y Adjust logits during the initial phase ... else Set αy π ν y , βy = 1, y, ν > 0 TLA and ADRW ... Θ Θ η ΘL(f, B) One SGD step Optional: anneal the learning rate η. Required when t = T0. ... Figure 3: (a) Training accuracy of CE+DRW (T0 = 160) |