2 citations · 3 across the 16 of their papers we have counts for
16 papers
CVPO: Enhancing LLM Reinforcement Learning Reasoning via Value-Variance Adaptation and Dynamic Curriculum Learning
Ziqi Jia, Yalu Ouyang, Bo Pang +5
Reinforcement learning (RL) has emerged as an effective method for enhancing the reasoning capabilities of large language models (LLMs). However, existing methods suffer from insuf…
Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation
Cai Zhou, Zijie Chen, Zian Li +7
Many generative tasks in chemistry and science involve distributions invariant to group symmetries (e.g., permutation and rotation). A common strategy enforces invariance and equiv…
Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics
Nima Shoghi, Yuxuan Liu, Yuning Shen +3
Molecular dynamics (MD) simulations remain the gold standard for studying protein dynamics, but their computational cost limits access to biologically relevant timescales. Recent g…
From Small to Large: Generalization Bounds for Transformers on Variable-Size Inputs
Anastasiia Alokhina, Pan Li
Transformers exhibit a notable property of \emph{size generalization}, demonstrating an ability to extrapolate from smaller token sets to significantly longer ones. This behavior h…
Branching Strategies Based on Subgraph GNNs: A Study on Theoretical Promise versus Practical Reality
Junru Zhou, Yicheng Wang, Pan Li
Graph Neural Networks (GNNs) have emerged as a promising approach for ``learning to branch'' in Mixed-Integer Linear Programming (MILP). While standard Message-Passing GNNs (MPNNs)…
FEVO: Financial Knowledge Expansion and Reasoning Evolution for Large Language Models
Bo Pang, Yalu Ouyang, Hangfei Xu +6
Advancements in reasoning for large language models (LLMs) have lead to significant performance improvements for LLMs in various fields such as mathematics and programming. However…
Towards A Universal Graph Structural Encoder
Jialin Chen, Haolan Zuo, Haoyu Peter Wang +3
Recent advancements in large-scale pre-training have shown the potential to learn generalizable representations for downstream tasks. In the graph domain, however, capturing and tr…
Structural Alignment Improves Graph Test-Time Adaptation
Hans Hao-Hsun Hsu, Shikun Liu, Han Zhao +1
Graph-based learning excels at capturing interaction patterns in diverse domains like recommendation, fraud detection, and particle physics. However, its performance often degrades…
Can Explanations Improve Recommendations? Evidence from Prediction-Informed Explanations
Yuyan Wang, Pan Li, Minmin Chen
Recommender systems are central to digital platforms, yet they face a fundamental trade-off between accuracy and explainability. Black-box models achieve strong performance but lac…
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
Kexin Zhang, Shuhan Liu, Song Wang +6
Distribution shifts on graphs -- the discrepancies in data distribution between training and employing a graph machine learning model -- are ubiquitous and often unavoidable in rea…
Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning
Jiajun Zhu, Siqi Miao, Rex Ying +1
The interpretability of machine learning models has gained increasing attention, particularly in scientific domains where high precision and accountability are crucial. This resear…
Pairwise Alignment Improves Graph Domain Adaptation
Shikun Liu, Deyu Zou, Han Zhao +1
Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model…
Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling
Yuhong Luo, Pan Li
Temporal graph representation learning (TGRL) is essential for modeling dynamic systems in real-world networks. However, traditional TGRL methods, despite their effectiveness, ofte…
High Pileup Particle Tracking with Object Condensation
Kilian Lieret, Gage DeZoort, Devdoot Chatterjee +3
Recent work has demonstrated that graph neural networks (GNNs) can match the performance of traditional algorithms for charged particle tracking while improving scalability to meet…
STRIDE: Structure and Embedding Distillation with Attention for Graph Neural Networks
Anshul Ahluwalia, Payman Behnam, Rohit Das +4
Recent advancements in Graph Neural Networks (GNNs) have led to increased model sizes to enhance their capacity and accuracy. Such large models incur high memory usage, latency, an…
GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts
Deyu Zou, Shikun Liu, Siqi Miao +3
Geometric deep learning (GDL) has gained significant attention in scientific fields, for its proficiency in modeling data with intricate geometric structures. However, very few wor…