2 citations · 2 across the 8 of their papers we have counts for
8 papers
Explicit Preference Optimization: No Need for an Implicit Reward Model
Xiangkun Hu, Lemin Kong, Tong He +1
The generated responses of large language models (LLMs) are often fine-tuned to human preferences through a process called reinforcement learning from human feedback (RLHF). As RLH…
Sparse Autoencoders, Again?
Yin Lu, Xuening Zhu, Tong He +1
Is there really much more to say about sparse autoencoders (SAEs)? Autoencoders in general, and SAEs in particular, represent deep architectures that are capable of modeling low-di…
Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions
Yixuan He, Aaron Sandel, David Wipf +3
How can we identify groups of primate individuals which could be conjectured to drive social structure? To address this question, one of us has collected a time series of data for…
New Desiderata for Direct Preference Optimization
Xiangkun Hu, Tong He, David Wipf
Large language models in the past have typically relied on some form of reinforcement learning with human feedback (RLHF) to better align model responses with human preferences. Ho…
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Minjie Wang, Quan Gan, David Wipf +17
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably…
BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization
Amber Yijia Zheng, Tong He, Yixuan Qiu +2
Bilevel optimization refers to scenarios whereby the optimal solution of a lower-level energy function serves as input features to an upper-level objective of interest. These optim…
MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy
Haitian Jiang, Renjie Liu, Zengfeng Huang +5
Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that…
Robust Angular Synchronization via Directed Graph Neural Networks
Yixuan He, Gesine Reinert, David Wipf +1
The angular synchronization problem aims to accurately estimate (up to a constant additive phase) a set of unknown angles from noisy measurements of…