41 citations · 104 across the 15 of their papers we have counts for
15 papers
Heavy-Tailed Regularization of Weight Matrices in Deep Neural Networks
Xuanzhe Xiao, Zeng Li, Chuanlong Xie +1
Unraveling the reasons behind the remarkable success and exceptional generalization capabilities of deep neural networks presents a formidable challenge. Recent insights from rando…
Fair-CDA: Continuous and Directional Augmentation for Group Fairness
Rui Sun, Fengwei Zhou, Zhenhua Dong +6
In this work, we propose {\it Fair-CDA}, a fine-grained data augmentation strategy for imposing fairness constraints. We use a feature disentanglement method to extract the feature…
Boosting Out-of-Distribution Detection with Multiple Pre-trained Models
Feng Xue, Zi He, Chuanlong Xie +2
Out-of-Distribution (OOD) detection, i.e., identifying whether an input is sampled from a novel distribution other than the training distribution, is a critical task for safely dep…
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
Qishi Dong, Awais Muhammad, Fengwei Zhou +5
Recent advances on large-scale pre-training have shown great potentials of leveraging a large set of Pre-Trained Models (PTMs) for improving Out-of-Distribution (OoD) generalizatio…
Boosting Out-of-distribution Detection with Typical Features
Yao Zhu, YueFeng Chen, Chuanlong Xie +6
Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD det…
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL
Siyi Hu, Chuanlong Xie, Xiaodan Liang +1
Cooperative multi-agent reinforcement learning (MARL) is making rapid progress for solving tasks in a grid world and real-world scenarios, in which agents are given different attri…
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
Muhammad Awais, Fengwei Zhou, Chuanlong Xie +3
Deep neural networks are susceptible to adversarially crafted, small and imperceptible changes in the natural inputs. The most effective defense mechanism against these examples is…
NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models
Hang Xu, Ning Kang, Gengwei Zhang +3
Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks. The common practice of fine-tuning is to adopt a…
Towards a Theoretical Framework of Out-of-Distribution Generalization
Haotian Ye, Chuanlong Xie, Tianle Cai +3
Generalization to out-of-distribution (OOD) data is one of the central problems in modern machine learning. Recently, there is a surge of attempts to propose algorithms that mainly…
Out-of-Distribution Generalization Analysis via Influence Function
Haotian Ye, Chuanlong Xie, Yue Liu +1
The mismatch between training and target data is one major challenge for current machine learning systems. When training data is collected from multiple domains and the target doma…
MetaAugment: Sample-Aware Data Augmentation Policy Learning
Fengwei Zhou, Jiawei Li, Chuanlong Xie +4
Automated data augmentation has shown superior performance in image recognition. Existing works search for dataset-level augmentation policies without considering individual sample…
Provable More Data Hurt in High Dimensional Least Squares Estimator
Zeng Li, Chuanlong Xie, Qinwen Wang
This paper investigates the finite-sample prediction risk of the high-dimensional least squares estimator. We derive the central limit theorem for the prediction risk when both the…
Risk Variance Penalization
Chuanlong Xie, Haotian Ye, Fei Chen +3
The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains. The variance risk extrapolation (V-REx) is a practical…
Fast Inference Procedures for Semivarying Coefficient Models via Local Averaging
Heng Peng, Chuanlong Xie, Jingxin Zhao
The semivarying coefficient models are widely used in the application of finance, economics, medical science and many other areas. The functional coefficients are commonly estimate…
An Adaptive-to-Model Test for Parametric Single-Index Errors-in-Variables Models
Hira L. Koul, Chuanlong Xie, Lixing Zhu
This paper provides some useful tests for fitting a parametric single-index regression model when covariates are measured with error and validation data is available. We propose tw…