1 citations · 1 across the 9 of their papers we have counts for
10 papers
Conformal Reliability: A New Evaluation Metric for Conditional Generation
Yachen Gao, Xinwei Sun, Yikai Wang +4
Conditional generative models have recently achieved remarkable success in various applications. However, a suitable metric for evaluating the reliability of these models, which ta…
Causal Inference for Network Autoregression Model: A Targeted Minimum Loss Estimation Approach
Yong Wu, Shuyuan Wu, Xinwei Sun +1
We study estimation of the average treatment effect (ATE) from a single network in observational settings with interference. The weak cross-unit dependence is modeled via an endoge…
Discovering Causal Relationships using Proxy Variables under Unmeasured Confounding
Yong Wu, Yanwei Fu, Shouyan Wang +2
Inferring causal relationships between variable pairs in the observational study is crucial but challenging, due to the presence of unmeasured confounding. While previous methods e…
Towards Reliable and Holistic Visual In-Context Learning Prompt Selection
Wenxiao Wu, Jing-Hao Xue, Chengming Xu +5
Visual In-Context Learning (VICL) has emerged as a prominent approach for adapting visual foundation models to novel tasks, by effectively exploiting contextual information embedde…
Revisiting Large Language Model Pruning using Neuron Semantic Attribution
Yizhuo Ding, Xinwei Sun, Yanwei Fu +1
Model pruning technique is vital for accelerating large language models by reducing their size and computational requirements. However, the generalizability of existing pruning met…
Adaptive Pruning of Pretrained Transformer via Differential Inclusions
Yizhuo Ding, Ke Fan, Yikai Wang +2
Large transformers have demonstrated remarkable success, making it necessary to compress these models to reduce inference costs while preserving their perfor-mance. Current compres…
A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs
Chang Wan, Ke Fan, Xinwei Sun +4
This paper introduces a promising alternative method for training Generative Adversarial Networks (GANs) on large-scale datasets with clear theoretical guarantees. GANs are typical…
Robust Network Learning via Inverse Scale Variational Sparsification
Zhiling Zhou, Zirui Liu, Chengming Xu +2
While neural networks have made significant strides in many AI tasks, they remain vulnerable to a range of noise types, including natural corruptions, adversarial noise, and low-re…
LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion
Jinyu Zhang, Yongchong Gu, Jianxiong Gao +5
This paper addresses the challenge of perceiving complete object shapes through visual perception. While prior studies have demonstrated encouraging outcomes in segmenting the visi…
Doubly Robust Proximal Causal Learning for Continuous Treatments
Yong Wu, Yanwei Fu, Shouyan Wang +1
Proximal causal learning is a promising framework for identifying the causal effect under the existence of unmeasured confounders. Within this framework, the doubly robust (DR) est…