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 with Feature and Distribution Evolvable Streams
Authors: Zhen-Yu Zhang, Peng Zhao, Yuan Jiang, Zhi-Hua Zhou
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on synthetic data verify the rationale of our proposed discrepancy measure, and extensive experiments on real-world tasks validate the effectiveness of our algorithm. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China. Correspondence to: Yuan Jiang <EMAIL>. |
| Pseudocode | No | The paper describes the algorithm steps and framework but does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | RFID Dataset (Hou et al., 2017) is real-time data streams collected by the RFID technique. Amazon Dataset (Mc Auley et al., 2015) contains the product s quality (label) from 2006 to 2008 according to the ratings of its users (feature). Reuters multilingual dataset (Amini et al., 2009) contains about 11K articles from 6 classes in 5 languages so that we can simulate the evolving stream by various languages. |
| Dataset Splits | No | The paper describes how evolving data is generated (e.g., '20% evolving data in each mini-batch') and how data is categorized or split for task generation, but it does not specify explicit train/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions general software components like 'SGD', 'cross-entropy loss', 'MLP', and 'ReLU', but it does not provide specific version numbers for any libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | For implementations of the EDM algorithm, we set the main classifiers (min-player) and auxiliary classifiers (maxplayer) in the adversarial network as two 5-layer MLP with Re LU as activation functions. The model is trained by SGD with a learning rate of 0.004 and regularization weight decay 0.005. |