Author name cluster

Yiqun Sun

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

AAAI Conference 2026 Conference Paper

Partially Shared Concept Bottleneck Models

Delong Zhao
Qiang Huang
Di Yan
Yiqun Sun
Jun Yu

Concept Bottleneck Models (CBMs) enhance interpretability by introducing a layer of human-understandable concepts between inputs and predictions. While recent methods automate concept generation using Large Language Models (LLMs) and Vision-Language Models (VLMs), they still face three fundamental challenges: poor visual grounding, concept redundancy, and the absence of principled metrics to balance predictive accuracy and concept compactness. We introduce PS-CBM, a Partially Shared CBM framework that addresses these limitations through three core components: (1) a multimodal concept generator that integrates LLM-derived semantics with exemplar-based visual cues; (2) a Partially Shared Concept Strategy that merges concepts based on activation patterns to balance specificity and compactness; and (3) Concept-Efficient Accuracy (CEA), a post-hoc metric that jointly captures both predictive accuracy and concept compactness. Extensive experiments on eleven diverse datasets show that PS-CBM consistently outperforms state-of-the-art CBMs, improving classification accuracy by 1.0%–7.4% and CEA by 2.0%–9.5%, while requiring significantly fewer concepts. These results underscore PS-CBM’s effectiveness in achieving both high accuracy and strong interpretability.

PDF Details DOI

ICLR Conference 2025 Conference Paper

A General Framework for Producing Interpretable Semantic Text Embeddings

Yiqun Sun
Qiang Huang
Yixuan Tang
Anthony K. H. Tung
Jun Yu 0002

Semantic text embedding is essential to many tasks in Natural Language Processing (NLP). While black-box models are capable of generating high-quality embeddings, their lack of interpretability limits their use in tasks that demand transparency. Recent approaches have improved interpretability by leveraging domain-expert-crafted or LLM-generated questions, but these methods rely heavily on expert input or well-prompt design, which restricts their generalizability and ability to generate discriminative questions across a wide range of tasks. To address these challenges, we introduce \algo{CQG-MBQA} (Contrastive Question Generation - Multi-task Binary Question Answering), a general framework for producing interpretable semantic text embeddings across diverse tasks. Our framework systematically generates highly discriminative, low cognitive load yes/no questions through the \algo{CQG} method and answers them efficiently with the \algo{MBQA} model, resulting in interpretable embeddings in a cost-effective manner. We validate the effectiveness and interpretability of \algo{CQG-MBQA} through extensive experiments and ablation studies, demonstrating that it delivers embedding quality comparable to many advanced black-box models while maintaining inherently interpretability. Additionally, \algo{CQG-MBQA} outperforms other interpretable text embedding methods across various downstream tasks. The source code is available at \url{https://github.com/dukesun99/CQG-MBQA}.

Details

EAAI Journal 2025 Journal Article

Assessment of hybrid kernel function in extreme support vector regression model for streamflow time series forecasting based on a bayesian estimator decomposition algorithm

Peng Shi
Lei Xu
Simin Qu
Hongshi Wu
Qiongfang Li
Yiqun Sun
Xiaoqiang Yang
Wei Gao

Diverse decomposition algorithms have been widely employed to streamflow time series forecasting. Their applications, however, are hindered by the plausible high accuracy in the overall decomposition-based framework. This paper firstly introduces a novel decomposition algorithm named Bayesian estimator of abrupt change, seasonality and trend (BEAST) into streamflow forecasting to alleviate the boundary effect. Practical samples are generated under the modified two-stage decomposition prediction (TSDP) framework. A hybrid kernel function, which benefits from two different standalone ones, is designed for kernel extreme support vector regression and the HKESVR model is trained on the samples using 10-fold cross-validation strategy. Comparative experiments are conducted on three monthly streamflow series from basins with diverse hydroclimatic conditions. The results in different lead times (1-, 3-, and 5-month-ahead) show that the BEAST algorithm imposes an average improvement of 5. 14% and 12. 25% for the root-mean-square error and Nash-Sutcliffe efficiency coefficient respectively on the standalone models and shares a comprehensive similar performance on the mean absolute percentage error. And the nonparametric test results reveal that the BEAST method shows a significant improvement on the comprehensive performance compared with a conventional decomposition method. By contrast, the differences between machine learning models are much smaller. The hybrid kernel function works well in some specific cases in which the standalone kernel function fails. The hybrid BEAST-HKESVR is reliable enough to rank the second place among the fifteen tested models. Finally, the effects of hyperparameters in the BEAST algorithm are discussed and relevant suggestions on them are provided.

Details DOI