13 citations · 20 across the 7 of their papers we have counts for
9 papers
Reliable Replay through Spatial Coherence in Online Continual Learning
Haixiang Sun, Jiefu Zhang, Yinghao He +4
Continually adapting models to new tasks requires retaining earlier knowledge under limited memory and computation. Experience replay addresses this challenge, but priorities based…
CARVE: Breaking Data Barriers in Chip Placement by Harnessing Reusable Expertise
Jiefu Zhang, Haixiang Sun, Yang Xu +2
Pretrained macro-placement policies can reduce repeated optimization across circuits, but deployment often exposes them to unfamiliar designs when the original training data are un…
Selection-Aware Stress Testing for Interactive Agents
Yang Xu, Chenang Li, Jiefu Zhang +3
Agent evaluations often use one benchmark to choose a workflow and then search for task types where its advantage weakens, so both conclusions are selected from the same data. We i…
Core-Halo Decomposition: Decentralizing Large-Scale Fixed-Point Problems
Haixiang, Yang Xu, Jiefu Zhang +4
We study solving large-scale fixed-point equation \(x^\star=\bar F(x^\star)\) with decomposition. Standard strict decomposition assigns each agent a disjoint block and evaluates up…
Towards Reliable LLM Evaluation: Correcting the Winner's Curse in Adaptive Benchmarking
Yang Xu, Jiefu Zhang, Haixiang Sun +3
Adaptive prompt and program search makes LLM evaluation selection-sensitive. Once benchmark items are reused inside tuning, the observed winner's score need not estimate the fresh-…
Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds
Jiefu Zhang, Yang Xu, Vaneet Aggarwal
Navigating safely through dense crowds requires collision avoidance that generalizes beyond the densities seen during training. Learning-based crowd navigation can break under out-…
Learning the mapping : the cost of finding the needle in a haystack
Jiefu Zhang, Leonardo Zepeda-Núñez, Yuan Yao +1
The task of using machine learning to approximate the mapping with seems to be a trivial one. Given the knowledge of the separa…
Universal approximation of symmetric and anti-symmetric functions
Jiequn Han, Yingzhou Li, Lin Lin +3
We consider universal approximations of symmetric and anti-symmetric functions, which are important for applications in quantum physics, as well as other scientific and engineering…
Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks
Leonardo Zepeda-Núñez, Yixiao Chen, Jiefu Zhang +3
The recently developed Deep Potential [Phys. Rev. Lett. 120, 143001, 2018] is a powerful method to represent general inter-atomic potentials using deep neural networks. The success…