10 citations · 11 across the 5 of their papers we have counts for
8 papers
LyapuFlow: Controlling Generative Flows with Lyapunov Feedback for Inverse Problems
Minseon Gwak, Hans Hao-Hsun Hsu, Danielle C. Maddix +1
Pretrained flow models are now widely used as generative priors in science and vision, where inference-time guidance enables test-time constraints without retraining. Existing meth…
Variational Streaming Flow: Probabilistic Forecasting in Physical Time
Hans Hao-Hsun Hsu, Minseon Gwak, Soon Hoe Lim +2
Probabilistic forecasting is important for predicting complex dynamical systems because intrinsic randomness and incomplete observations can cause the same observed state to evolve…
Can LLM Agents Simulate Dynamic Networks? A Case Study on Email Networks with Phishing Synthesis
Siqi Miao, Ziyang Chen, Yuhong Luo +4
While Large Language Model (LLM) multi-agent systems (MAS) offer a transformative approach to simulating human behavior in complex systems, it remains largely unexplored whether th…
Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective
Yinan Huang, Hans Hao-Hsun Hsu, Junran Wang +2
Sequential probabilistic inference from streaming observations requires modeling distributions over future trajectories as new observations arrive. Although diffusion and flow-matc…
Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness
Rongzhe Wei, Peizhi Niu, Hans Hao-Hsun Hsu +9
Machine unlearning techniques aim to mitigate unintended memorization in large language models (LLMs). However, existing approaches predominantly focus on the explicit removal of i…
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…
A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs
Hans Hao-Hsun Hsu, Yuesong Shen, Daniel Cremers
Current graph neural networks (GNNs) that tackle node classification on graphs tend to only focus on nodewise scores and are solely evaluated by nodewise metrics. This limits uncer…
What Makes Graph Neural Networks Miscalibrated?
Hans Hao-Hsun Hsu, Yuesong Shen, Christian Tomani +1
Given the importance of getting calibrated predictions and reliable uncertainty estimations, various post-hoc calibration methods have been developed for neural networks on standar…