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..
Efficient Online Set-valued Classification with Bandit Feedback
Authors: Zhou Wang, Xingye Qiao
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of BCCP is empirically validated using three different score functions and two policies (for pulling arm) across three datasets, demonstrating the versatility and efficacy of our proposed framework. |
| Researcher Affiliation | Academia | 1Department of Mathematics and Statistics, Binghamton University, New York, USA. |
| Pseudocode | Yes | Algorithm 1 Bandit Conformal; Algorithm 2 Bandit Conformal with Experts |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for the methodology or a link to a code repository. |
| Open Datasets | Yes | Our experimental setup includes the CIFAR10, CIFAR100 (with 20 coarser labels), and SVHN datasets, each undergoing 5 replications. |
| Dataset Splits | Yes | In the split conformal method (Papadopoulos et al., 2002; Lei et al., 2013), the index set I associated with the original dataset D is partitioned into two disjoint subsets: the training part Itr and the calibration part Ical. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions employing the ResNet50 architecture and the ADAM optimizer but does not provide specific version numbers for these or other software libraries. |
| Experiment Setup | Yes | Consistently throughout the study, we maintain a non-coverage rate α = 0.05. For computational efficiency, the model training is performed on data batches of size 256, utilizing the ADAM optimizer with a learning rate of η1 = 10 4 in the model training phase. The entire online learning process spans T = 6000 iterations around. |