11 papers · 1 filter
Resilient Concurrent Causal Discovery for Topological Event Sequences
Jiyu Tian, Junhao Dong, Mingchu Li +5
Causal discovery on topological event sequences is crucial for ensuring the reliability of networks. However, existing methods struggle to capture the complex causal relationships…
Zhinv: Real-time hub-height wind field reconstruction using only local sparse observations
Zongwei Zhang, Chin Chun Ooi, Lianlei Lin +8
The high proportion of wind power connected to the grid places higher demands on fine-grained knowledge of regional wind fields. Since the wind information directly obtainable in a…
Possibilistic Predictive Uncertainty for Deep Learning
Yao Ni, Jeremie Houssineau, Yew-Soon Ong +1
Deep neural networks achieve impressive results across diverse applications, yet their overconfidence on unseen inputs necessitates reliable epistemic uncertainty modeling. Existin…
Learning with Foresight: Enhancing Neural Routing Policy via Multi-Node Lookahead Prediction
Xia Jiang, Yaoxin Wu, Yew-Soon Ong +1
Neural policies have shown promise in solving vehicle routing problems due to their reduced reliance on handcrafted heuristics. However, current training paradigms suffer from a fu…
Flow-Direct: Feedback-Efficient and Reusable Guidance for Flow Models via Non-Parametric Guidance Field
Kim Yong Tan, Yueming Lyu, Ivor Tsang +1
Training-free guidance enables pre-trained diffusion and flow models to optimize application-specific objectives using feedback from external black-box reward functions. However, e…
Amortized Multi-Objective Optimization Across Tasks with Generative Solution Modeling
Tingyang Wei, Jiao Liu, Abhishek Gupta +3
Many real-world applications require solving families of expensive multi-objective optimization problems~(EMOPs) under varying operational conditions. This can be formulated as par…