Joker: Joint Optimization Framework for Lightweight Kernel Machines
Authors: Junhong Zhang, Zhihui Lai
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that Joker saves up to 90% memory but achieves comparable training time and performance (or even better) than the state-of-the-art methods. Table 4 shows the result of the performance comparison. |
| Researcher Affiliation | Academia | 1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China 2Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen, 518060, China. Correspondence to: Zhihui Lai <lai zhi EMAIL>. |
| Pseudocode | Yes | Algorithm 1: Trust region (twice-differentialable f), Algorithm 2: Truncated CG-Steihaug, Algorithm 3: DBCD-TR for problem (4) |
| Open Source Code | Yes | The implementation2 of Joker is based on PyTorch without extra acceleration libraries. 2Code available at GitHub: https://github.com/Apple-Zhang/Joker-paper. |
| Open Datasets | Yes | Million-song dataset (MSD) (Bertin-Mahieux et al., 2011). This dataset contains audio features for year prediction. Available at https://archive.ics.uci.edu/dataset/203/yearpredictionmsd. Household Electric Power Consumption (HEPC) dataset: It is the same as the House Elec dataset used in (Lin et al., 2024), available using the python package https://github.com/treforevans/uci_datasets. Supersymmetric particle classification (SUSY) dataset (Baldi et al., 2014): This is a binary classification task to distinguish between supersymmetric particles and background process. Available at https://archive.ics. uci.edu/dataset/279/susy. HIGGS dataset (Baldi et al., 2014): This is a binary classification task to distinguish between the Higgs boson and the background process. Available at https://archive.ics.uci.edu/dataset/280/higgs. CIFAR-5M dataset (Nakkiran et al., 2021): This is a generated dataset based on CIFAR-10. Available at https: //github.com/preetum/cifar5m. |
| Dataset Splits | Yes | Table A.1. Summary of datasets used in experiments Data Split ... MSD 90% train, 10% test ... HEPC 90% train, 10% test ... SUSY 80% train, 20% test ... HIGGS 80% train, 20% test ... CIFAR-5M 80% train, 20% test |
| Hardware Specification | Yes | We implemented KRR, KLR, SVM, etc. based on Joker, and conducted experiments with a single RTX 3080 (10GB). To highlight that Joker can obtain promising performance under a limited computational budget, we conduct experiments on a machine with a single consumer GPU (NVIDIA RTX 3080, 10GB) and 64GB RAM. |
| Software Dependencies | No | The implementation2 of Joker is based on PyTorch without extra acceleration libraries. Explanation: The paper mentions PyTorch as a dependency but does not specify its version number or versions for any other key software components, which is required for reproducibility. |
| Experiment Setup | Yes | The regularization parameter λ in Joker is tuned from {2i : i = 7, 6, ..., 7} via grid search. ... In most cases, we employ the inexact Joker models with the block size |B| = 512 ... We also increase the block size to 1024 for Joker-KLR to accelerate convergence. ... Details of further parameter settings are shown in Appendix C. ... Table A.3. The major hyperparameters of the models in the experiments. |