8 citations · 8 across the 5 of their papers we have counts for
5 papers
When Evidence is Sparse: Weakly Supervised Early Failure Alerting in Dialogs and LLM-Agent Trajectories
Avinash Baidya, Xinran Liang, Ruocheng Guo +2
Early failure alerting requires deciding, while a dialog or agent trajectory is still unfolding, whether to flag it as likely to fail. This is challenging because supervision is ty…
FWeb3: A Practical Incentive-Aware Federated Learning Framework
Peishen Yan, Shuang Liang, Yang Hua +9
Federated learning (FL) enables collaborative model training over distributed private data. However, sustaining open participation requires incentive mechanisms that compensate con…
IC-Effect: Precise and Efficient Video Effects Editing via In-Context Learning
Yuanhang Li, Yiren Song, Junzhe Bai +4
We propose \textbf{IC-Effect}, an instruction-guided, DiT-based framework for few-shot video VFX editing that synthesizes complex effects (\eg flames, particles and cartoon charact…
ALP: Action-Aware Embodied Learning for Perception
Xinran Liang, Anthony Han, Wilson Yan +2
Current methods in training and benchmarking vision models exhibit an over-reliance on passive, curated datasets. Although models trained on these datasets have shown strong perfor…
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
Xinran Liang, Katherine Shu, Kimin Lee +1
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible rewar…