59 citations · 264 across the 38 of their papers we have counts for
12 papers · 1 filter
Leveraging Graph Diffusion Models for Network Refinement Tasks
Puja Trivedi, Ryan Rossi, David Arbour +7
Most real-world networks are noisy and incomplete samples from an unknown target distribution. Refining them by correcting corruptions or inferring unobserved regions typically imp…
Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances
Zhendong Chu, Ruiyi Zhang, Tong Yu +4
To achieve state-of-the-art performance, one still needs to train NER models on large-scale, high-quality annotated data, an asset that is both costly and time-intensive to accumul…
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search
Yuchen Zhuang, Xiang Chen, Tong Yu +5
Large language models (LLMs) have demonstrated powerful decision-making and planning capabilities in solving complicated real-world problems. LLM-based autonomous agents can intera…
Online Corrupted User Detection and Regret Minimization
Zhiyong Wang, Jize Xie, Tong Yu +2
In real-world online web systems, multiple users usually arrive sequentially into the system. For applications like click fraud and fake reviews, some users can maliciously perform…
Bias and Fairness in Large Language Models: A Survey
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow +6
Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touc…
Fairness-Aware Graph Neural Networks: A Survey
April Chen, Ryan A. Rossi, Namyong Park +6
Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks.…
FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedback
Ashish Singh, Ashutosh Singh, Prateek Agarwal +10
Captions are crucial for understanding scientific visualizations and documents. Existing captioning methods for scientific figures rely on figure-caption pairs extracted from docum…
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding
Junda Wu, Tong Yu, Rui Wang +6
Soft prompt tuning achieves superior performances across a wide range of few-shot tasks. However, the performances of prompt tuning can be highly sensitive to the initialization of…
Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
Jian Chen, Ruiyi Zhang, Tong Yu +4
Learning from noisy labels is an important and long-standing problem in machine learning for real applications. One of the main research lines focuses on learning a label corrector…
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning
Kaige Xie, Tong Yu, Haoliang Wang +6
In real-world scenarios, labeled samples for dialogue summarization are usually limited (i.e., few-shot) due to high annotation costs for high-quality dialogue summaries. To effici…
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
Shuai Li, Zhao Song, Yu Xia +2
Large language models (LLMs) are known for their exceptional performance in natural language processing, making them highly effective in many human life-related or even job-related…
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model
Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim +4
We consider dynamic pricing strategies in a streamed longitudinal data set-up where the objective is to maximize, over time, the cumulative profit across a large number of customer…