25 citations · 55 across the 9 of their papers we have counts for
9 papers
Reinforcing Chain-of-Thought Reasoning with Self-Evolving Rubrics
Leheng Sheng, Wenchang Ma, Ruixin Hong +3
Despite chain-of-thought (CoT) playing crucial roles in LLM reasoning, directly rewarding it is difficult: training a reward model demands heavy human labeling efforts, and static…
When to Memorize and When to Stop: Gated Recurrent Memory for Long-Context Reasoning
Leheng Sheng, Yongtao Zhang, Wenchang Ma +6
While reasoning over long context is crucial for various real-world applications, it remains challenging for large language models (LLMs) as they suffer from performance degradatio…
General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout
An Zhang, Wenchang Ma, Pengbo Wei +2
Graph neural networks (GNNs) have shown impressive performance in recommender systems, particularly in collaborative filtering (CF). The key lies in aggregating neighborhood inform…
Robust Collaborative Filtering to Popularity Distribution Shift
An Zhang, Wenchang Ma, Jingnan Zheng +2
In leading collaborative filtering (CF) models, representations of users and items are prone to learn popularity bias in the training data as shortcuts. The popularity shortcut tri…
Discovering Dynamic Causal Space for DAG Structure Learning
Fangfu Liu, Wenchang Ma, An Zhang +3
Discovering causal structure from purely observational data (i.e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine lear…
Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting
An Zhang, Fangfu Liu, Wenchang Ma +3
Under stringent model type and variable distribution assumptions, differentiable score-based causal discovery methods learn a directed acyclic graph (DAG) from observational data b…
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
An Zhang, Wenchang Ma, Xiang Wang +1
Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences. However, most current debiasing strategie…
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation
Yuning Mao, Wenchang Ma, Deren Lei +2
Prior studies on text-to-text generation typically assume that the model could figure out what to attend to in the input and what to include in the output via seq2seq learning, wit…
CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation
Wenchang Ma, Ryuichi Takanobu, Minlie Huang
Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate re…