Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization
Authors: Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,539 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,366 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments verify the efficiency of the proposed algorithm in comparison with state-of-the-art OFO algorithms.We evaluate the performance on 25 binary classification tasks from seven benchmark datasets (covtype, webspam, a9a, ijcnn1, w8a, sensorless, protein). All the datasets involved are downloaded from the LIBSVM repository [5]. Each dataset is divided into three parts (1:1:1) for online training, online validation and offline testing. |
| Researcher Affiliation | Academia | Department of Computer Science, The University of Iowa, Iowa City, IA 52242, USA Department of Management Sciences, The University of Iowa, Iowa City, IA 52242, USA mingrui-liu, EMAIL |
| Pseudocode | Yes | Algorithm 1 FOFO(n) and Algorithm 2 SFO(w, θ, bĪ, T, Îŗ, R, T0) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | All the datasets involved are downloaded from the LIBSVM repository [5]. |
| Dataset Splits | Yes | Each dataset is divided into three parts (1:1:1) for online training, online validation and offline testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using specific algorithms and loss functions (e.g., 'logistic loss', 'SGD') but does not specify any software libraries or dependencies with version numbers. |
| Experiment Setup | Yes | For FOFO, OFO, and LR, we tune the initial step parameter for learning the posterior probability in the range 2[ 4:1:4]. For STAMP and OMCSL, the stepsize parameter is also tuned in 2[ 8:1:4]. For OMCSL, we use 10 settings for the weights and learn 10 classifiers online. |