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..
Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions
Authors: Quanqi Hu, Qi Qi, Zhaosong Lu, Tianbao Yang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we conduct experiments on positive-unlabeled (PU) learning and partial area under ROC curve (p AUC) optimization with an adversarial fairness regularizer to validate the effectiveness of our proposed algorithms. |
| Researcher Affiliation | Academia | 1 Department of Computer Science & Engineering, Texas A&M University 2 Department of Computer Science, The University of Iowa 3 Department of Industrial and Systems Engineering, University of Minnesota |
| Pseudocode | Yes | Algorithm 1 Stochastic Moreau Envelope Approximate Gradient Method (SMAG) ... Algorithm 2 SMAG for DWC Optimization ... Algorithm 3 SMAG for WCSC Min-Max Optimization |
| Open Source Code | Yes | The code is included in the supplemental material. |
| Open Datasets | Yes | We use four multi-class classification datasets, Fashion-MNIST [36], MNIST [5] CIFAR10 [14] and FER2013 [6]. |
| Dataset Splits | Yes | We divide the dataset into training, validation, and test data with an 80%/10%/10% split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For all datasets, we use a batch size of 64 and set πp = 0.5. We train 40 epochs and decay the learning rate by 10 at epoch 12 and 24. The learning rates of SGD, SDCA, SSDC-SPG and SSDC-Adagrad, the learning rate of the inner loop of SBCD (i.e., µηt/(µ + ηt)), and η1 in SMAG are all tuned from {10, 1, 0.2, 0.1, 0.01, 0.001}. The learning rate of the outer loop in SDCA and η0 in SMAG are tuned from {0.1, 0.5, 0.9}. The numbers of inner loops for all double-loop methods are tuned from {2, 5, 10}. The µ in SBCD, 1/γ in SSDC-SPG and SSDC-Adagrad, γ in SMAG are tuned in {0.05, 0.1, 0.2, 0.5, 1, 2}. |