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 [1].
Multi-Objective Online Learning
Authors: Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Lihong Gu, Xiaodong Zeng, Wenwu Zhu
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several real-world datasets verify the effectiveness of the proposed algorithm. |
| Researcher Affiliation | Collaboration | Jiyan Jiang Tsinghua University Beijing, China EMAIL Wenpeng Zhang Ant Group Beijing, China EMAIL Shiji Zhou Tsinghua University Beijing, China EMAIL Lihong Gu, Xiaodong Zeng Ant Group Hangzhou, China EMAIL Wenwu Zhu Tsinghua University Beijing, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Doubly Regularized Online Mirror Multiple Descent (DR-OMMD) |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | We use two large-scale online benchmark datasets. (i) protein is a bioinformatics dataset for protein type classification (Wang, 2002), which has 17 thousand instances with 357 features. (ii) covtype is a biological dataset collected from a non-stationary environment for forest cover type prediction (Blackard & Dean, 1999), which has 50 thousand instances with 54 features. We use Multi MNIST (Sabour et al., 2017) |
| Dataset Splits | No | The paper does not provide specific numerical train/validation/test dataset splits. It states: 'In the online setting, samples arrive in a sequential manner, which is different from offline experiments where sample batches are randomly sampled from the training set.' |
| Hardware Specification | Yes | All runs are deployed on Xeon(R) E5-2699 @ 2.2GHz. |
| Software Dependencies | No | The paper mentions software components like 'Le Net' and 'Adam' but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The learning rates are decided by a grid search over {0.1, 0.2, . . . , 3.0}. For DR-OMMD, the parameter αt is simply set as 0.1. For linearization, we examine different weights (0.25, 0.75), (0.5, 0.5), and (0.75, 0.25). Learning rates in all methods are selected via grid search over {0.0001, 0.001, 0.01, 0.1}. For DR-OMMD, αt is set according to Theorem 1, and the initial weights are simply set as λ0 = (0.5, 0.5). |