1 citations · 1 across the 7 of their papers we have counts for
8 papers
MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery
Dong Li, Zhengzhang Chen, Xujiang Zhao +5
Uncovering causal structures from observational data is crucial for understanding complex systems and making informed decisions. While reinforcement learning (RL) has shown promise…
Out-of-Distribution Detection with Positive and Negative Prompt Supervision Using Large Language Models
Zhixia He, Chen Zhao, Minglai Shao +5
Out-of-distribution (OOD) detection is committed to delineating the classification boundaries between in-distribution (ID) and OOD images. Recent advances in vision-language models…
SolverLLM: Leveraging Test-Time Scaling for Optimization Problem via LLM-Guided Search
Dong Li, Xujiang Zhao, Linlin Yu +7
Large Language Models (LLMs) offer promising capabilities for tackling complex reasoning tasks, including optimization problems. However, existing methods either rely on prompt eng…
Evidential Uncertainty Probes for Graph Neural Networks
Linlin Yu, Kangshuo Li, Pritom Kumar Saha +2
Accurate quantification of both aleatoric and epistemic uncertainties is essential when deploying Graph Neural Networks (GNNs) in high-stakes applications such as drug discovery an…
Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization?
Jianfeng He, Runing Yang, Linlin Yu +5
Text summarization, a key natural language generation (NLG) task, is vital in various domains. However, the high cost of inaccurate summaries in risk-critical applications, particu…
Predictive Uncertainty Quantification for Bird's Eye View Segmentation: A Benchmark and Novel Loss Function
Linlin Yu, Bowen Yang, Tianhao Wang +2
The fusion of raw sensor data to create a Bird's Eye View (BEV) representation is critical for autonomous vehicle planning and control. Despite the growing interest in using deep l…
Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization
Russell Alan Hart, Linlin Yu, Yifei Lou +1
Deep neural networks have achieved significant success in the last decades, but they are not well-calibrated and often produce unreliable predictions. A large number of literature…
Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission
Jianfeng He, Linlin Yu, Shuo Lei +2
Sequential labeling is a task predicting labels for each token in a sequence, such as Named Entity Recognition (NER). NER tasks aim to extract entities and predict their labels giv…