463 citations · 1.1k across the 22 of their papers we have counts for
23 papers
Tracing the Evidence: Faithful Token Attribution Through Vision-Language Reasoning
Bowen Yuan, Danny Wang, Ruihong Qiu +2
Large vision-language models (LVLMs) exhibit strong reasoning capabilities, yet the visual and textual evidence supporting the generated responses remains difficult to identify. Fa…
Break the Block: Dynamic-size Reasoning Blocks for Diffusion Large Language Models via Monotonic Entropy Descent with Reinforcement Learning
Yan Jiang, Ruihong Qiu, Zi Huang
Recent diffusion large language models (dLLMs) have demonstrated both effectiveness and efficiency in reasoning via a block-based semi-autoregressive generation paradigm. Despite t…
GFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning
Yan Jiang, Ruihong Qiu, Zi Huang
Graph prompt tuning has shown great potential in graph learning by introducing trainable prompts to enhance the model performance in conventional single-domain scenarios. Recent re…
What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decomposition
Danny Wang, Ruihong Qiu, Zi Huang
Graph neural networks are widely used for node classification, but they remain vulnerable to out-of-distribution (OOD) shifts in node features and graph structure. Prior work estab…
Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models
Yan Jiang, Ruihong Qiu, Zi Huang
Recently, reinforcement learning (RL) has been widely applied during post-training for diffusion large language models (dLLMs) to enhance reasoning with block-wise semi-autoregress…
When to Commit? Towards Variable-Size Self-Contained Blocks for Discrete Diffusion Language Models
Danny Wang, Ruihong Qiu, Zi Huang
Discrete diffusion language models (dLLMs) enable parallel token updates with bidirectional attention, yet practical generation typically adopts blockwise semi-autoregressive decod…
TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only
Yilun Liu, Ruihong Qiu, Zi Huang
Zero-shot reasoning on text-rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure without task-specific supervis…
Does Homophily Help in Robust Test-time Node Classification?
Yan Jiang, Ruihong Qiu, Zi Huang
Homophily, the tendency of nodes from the same class to connect, is a fundamental property of real-world graphs, underpinning structural and semantic patterns in domains such as ci…
CaT: Balanced Continual Graph Learning with Graph Condensation
Yilun Liu, Ruihong Qiu, Zi Huang
Continual graph learning (CGL) is purposed to continuously update a graph model with graph data being fed in a streaming manner. Since the model easily forgets previously learned k…
Balanced and Explainable Social Media Analysis for Public Health with Large Language Models
Yan Jiang, Ruihong Qiu, Yi Zhang +1
As social media becomes increasingly popular, more and more public health activities emerge, which is worth noting for pandemic monitoring and government decision-making. Current t…
UQ at #SMM4H 2023: ALEX for Public Health Analysis with Social Media
Yan Jiang, Ruihong Qiu, Yi Zhang +1
As social media becomes increasingly popular, more and more activities related to public health emerge. Current techniques for public health analysis involve popular models such as…
Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation
Ruihong Qiu, Zi Huang, Hongzhi Yin
Overfitting has long been considered a common issue to large neural network models in sequential recommendation. In our study, an interesting phenomenon is observed that overfittin…
GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning
Zhi Chen, Yadan Luo, Sen Wang +2
Generalized Zero-Shot Learning (GZSL) aims to recognize images from both the seen and unseen classes by transferring semantic knowledge from seen to unseen classes. It is a promisi…
Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
Ruihong Qiu, Zi Huang, Hongzhi Yin +1
Recent advancements of sequential deep learning models such as Transformer and BERT have significantly facilitated the sequential recommendation. However, according to our study, t…
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation
Ruihong Qiu, Zi Huang, Hongzhi Yin
The sequential recommendation aims to recommend items, such as products, songs and places, to users based on the sequential patterns of their historical records. Most existing sequ…
Learning to Diversify for Single Domain Generalization
Zijian Wang, Yadan Luo, Ruihong Qiu +2
Domain generalization (DG) aims to generalize a model trained on multiple source (i.e., training) domains to a distributionally different target (i.e., test) domain. In contrast to…
CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
Ruihong Qiu, Sen Wang, Zhi Chen +2
Visually-aware recommendation on E-commerce platforms aims to leverage visual information of items to predict a user's preference. It is commonly observed that user's attention to…
Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks
Ruihong Qiu, Zi Huang, Jingjing Li +1
Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i.e., a sequence of interactions between a user and m…
Exploiting Positional Information for Session-based Recommendation
Ruihong Qiu, Zi Huang, Tong Chen +1
For present e-commerce platforms, session-based recommender systems are developed to predict users' preference for next-item recommendation. Although a session can usually reflect…
Mitigating Generation Shifts for Generalized Zero-Shot Learning
Zhi Chen, Yadan Luo, Sen Wang +3
Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e.g., attributes) to recognize the seen and unseen samples, where unseen classes are not obser…
Semantics Disentangling for Generalized Zero-Shot Learning
Zhi Chen, Yadan Luo, Ruihong Qiu +4
Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not observable during training. To bridge the gap between the seen and uns…
GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
Ruihong Qiu, Hongzhi Yin, Zi Huang +1
Streaming session-based recommendation (SSR) is a challenging task that requires the recommender system to do the session-based recommendation (SR) in the streaming scenario. In th…
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
Ruihong Qiu, Jingjing Li, Zi Huang +1
Predicting a user's preference in a short anonymous interaction session instead of long-term history is a challenging problem in the real-life session-based recommendation, e.g., e…