9 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…
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…
The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation
Yong Wu, Mingzhou Liu, Jing Yan +4
Assessing causal effects in the presence of unobserved confounding is a challenging problem. Existing studies leveraged proxy variables or multiple treatments to adjust for the con…
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…