4 citations · 19 across the 18 of their papers we have counts for
10 papers · 1 filter
RPO: Decoupling Rollout and Inference Policies for LLM Reasoning
Jingchu Wang, Bingbing Xu, Yige Yuan +4
Existing reinforcement learning methods for LLM reasoning implicitly assume that the policy generating training trajectories should coincide with the one producing inference respon…
InfoNCE is a Free Lunch for Semantically guided Graph Contrastive Learning
Zixu Wang, Bingbing Xu, Yige Yuan +2
As an important graph pre-training method, Graph Contrastive Learning (GCL) continues to play a crucial role in the ongoing surge of research on graph foundation models or LLM as e…
Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment
Teng Xiao, Yige Yuan, Huaisheng Zhu +2
We study the problem of aligning large language models (LLMs) with human preference data. Contrastive preference optimization has shown promising results in aligning LLMs with avai…
MITA: Bridging the Gap between Model and Data for Test-time Adaptation
Yige Yuan, Bingbing Xu, Teng Xiao +4
Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. However, existing mainstream TTA methods, predominantly operating at ba…
Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning
Zixu Wang, Bingbing Xu, Yige Yuan +2
Graph contrastive learning (GCL), standing as the dominant paradigm in the realm of graph pre-training, has yielded considerable progress. Nonetheless, its capacity for out-of-dist…
TEA: Test-time Energy Adaptation
Yige Yuan, Bingbing Xu, Liang Hou +3
Test-time adaptation (TTA) aims to improve model generalizability when test data diverges from training distribution, offering the distinct advantage of not requiring access to tra…
PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion
Yige Yuan, Bingbing Xu, Bo Lin +4
The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones. Current…
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective
Yige Yuan, Bingbing Xu, Huawei Shen +4
Graph contrastive learning (GCL) emerges as the most representative approach for graph representation learning, which leverages the principle of maximizing mutual information (Info…
Hierarchical Estimation for Effective and Efficient Sampling Graph Neural Network
Yang Li, Bingbing Xu, Qi Cao +2
Improving the scalability of GNNs is critical for large graphs. Existing methods leverage three sampling paradigms including node-wise, layer-wise and subgraph sampling, then desig…
Augmentation-Aware Self-Supervision for Data-Efficient GAN Training
Liang Hou, Qi Cao, Yige Yuan +7
Training generative adversarial networks (GANs) with limited data is challenging because the discriminator is prone to overfitting. Previously proposed differentiable augmentation…