54 citations · 160 across the 23 of their papers we have counts for
7 papers · 1 filter
TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins
Yuxiang Luo, Haonan Long, Chen Wang +6
Fine-tuning large language models (LLMs) is compute-intensive and error-prone: model performance depends sensitively on data quality and hyperparameter choices, and naïve runs can…
Structural-Entropy-Based Sample Selection for Efficient and Effective Learning
Tianchi Xie, Jiangning Zhu, Guozu Ma +4
Sample selection improves the efficiency and effectiveness of machine learning models by providing informative and representative samples. Typically, samples can be modeled as a sa…
RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers
Zhen Li, Weikai Yang, Jun Yuan +6
The high performance of tree ensemble classifiers benefits from a large set of rules, which, in turn, makes the models hard to understand. To improve interpretability, existing met…
Foundation Models Meet Visualizations: Challenges and Opportunities
Weikai Yang, Mengchen Liu, Zheng Wang +1
Recent studies have indicated that foundation models, such as BERT and GPT, excel in adapting to a variety of downstream tasks. This adaptability has established them as the domina…
Diagnosing Ensemble Few-Shot Classifiers
Weikai Yang, Xi Ye, Xingxing Zhang +6
The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance. When the performance is not satisfactory, it is usually diffi…
Interactive Steering of Hierarchical Clustering
Weikai Yang, Xiting Wang, Jie Lu +2
Hierarchical clustering is an important technique to organize big data for exploratory data analysis. However, existing one-size-fits-all hierarchical clustering methods often fail…
Diagnosing Concept Drift with Visual Analytics
Weikai Yang, Zhen Li, Mengchen Liu +4
Concept drift is a phenomenon in which the distribution of a data stream changes over time in unforeseen ways, causing prediction models built on historical data to become inaccura…