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..
Learning to Help in Multi-Class Settings
Authors: Yu Wu, Yansong Li, Zeyu Dong, Nitya Sathyavageeswaran, Anand Sarwate
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our proposed methods offer an efficient and practical solution for multi-class classification in resource-constrained environments. |
| Researcher Affiliation | Academia | Rutgers University, University of Illinois Chicago, Stony Brook University |
| Pseudocode | Yes | Algorithm 1 Optimization With Our Surrogate Loss Function |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing code or a link to a code repository. No mention of code in supplementary materials. |
| Open Datasets | Yes | In this section, we test the proposed surrogate loss function in equation 7 and algorithms for different settings on CIFAR-10 (Krizhevsky & Hinton, 2009), SVHN (Netzer et al., 2011) , and CIFAR-100 (Krizhevsky & Hinton, 2009) datasets. |
| Dataset Splits | Yes | CIFAR-10 consists of 32 × 32 color images drawn from 10 classes and is split into 50000 training and 10000 testing images. |
| Hardware Specification | Yes | The experiments are conducted in RTX 3090. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | To reduce the computation, we use Stochastic Gradient Descent (SGD) for presentation. Specifically, we choose ce from an interval between [0, 0.5] with fixed inaccuracy costs c1 = 1 and c1 = 1.25. In our experiments, the base network structure for the client classifier and the rejector is Le Net-5, and the server classifier is either Alex Net or Vi T. |