b'Xinwei Sun'

research

∙ 08/04/2023

DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization

Time series remains one of the most challenging modalities in machine le...

0 Wang Lu, et al. ∙

research

∙ 05/09/2023

Causal Discovery with Unobserved Variables: A Proxy Variable Approach

Discovering causal relations from observational data is important. The e...

0 Mingzhou Liu, et al. ∙

research

∙ 05/09/2023

Causal Discovery from Subsampled Time Series with Proxy Variables

Inferring causal structures from time series data is the central interes...

0 Mingzhou Liu, et al. ∙

research

∙ 01/02/2023

Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels

A noisy training set usually leads to the degradation of the generalizat...

0 Yikai Wang, et al. ∙

research

∙ 11/29/2022

PatchMix Augmentation to Identify Causal Features in Few-shot Learning

The task of Few-shot learning (FSL) aims to transfer the knowledge learn...

0 Chengming Xu, et al. ∙

research

∙ 09/15/2022

Generalized Representations Learning for Time Series Classification

Time series classification is an important problem in real world. Due to...

0 Wang Lu, et al. ∙

research

∙ 07/24/2022

A New Causal Decomposition Paradigm towards Health Equity

Causal decomposition has provided a powerful tool to analyze health disp...

0 Xinwei Sun, et al. ∙

research

∙ 04/21/2022

Domain Invariant Model with Graph Convolutional Network for Mammogram Classification

Due to its safety-critical property, the image-based diagnosis is desire...

8 Churan Wang, et al. ∙

research

∙ 03/15/2022

Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels

Noisy training set usually leads to the degradation of generalization an...

0 Yikai Wang, et al. ∙

research

∙ 10/08/2021

Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition

Context, as referred to situational factors related to the object of int...

2 Mingzhou Liu, et al. ∙

research

∙ 07/05/2021

Causally Invariant Predictor with Shift-Robustness

This paper proposes an invariant causal predictor that is robust to dist...

0 Xiangyu Zheng, et al. ∙

research

∙ 03/30/2021

Causal Hidden Markov Model for Time Series Disease Forecasting

We propose a causal hidden Markov model to achieve robust prediction of ...

0 Jing Li, et al. ∙

research

∙ 03/30/2021

Controlling the False Discovery Rate in Structural Sparsity: Split Knockoffs

Controlling the False Discovery Rate (FDR) in a variable selection proce...

0 Yang Cao, et al. ∙

research

∙ 12/21/2020

Disease Forecast via Progression Learning

Forecasting Parapapillary atrophy (PPA), i.e., a symptom related to most...

0 Botong Wu, et al. ∙

research

∙ 12/19/2020

Identifying Invariant Texture Violation for Robust Deepfake Detection

Existing deepfake detection methods have reported promising in-distribut...

0 Xinwei Sun, et al. ∙

research

∙ 11/04/2020

Latent Causal Invariant Model

Current supervised learning can learn spurious correlation during the da...

0 Xinwei Sun, et al. ∙

research

∙ 11/03/2020

Learning Causal Semantic Representation for Out-of-Distribution Prediction

Conventional supervised learning methods, especially deep ones, are foun...

1 Chang Liu, et al. ∙

research

∙ 09/30/2020

Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification

Mammogram benign or malignant classification with only image-level label...

1 Chu-ran Wang, et al. ∙

research

∙ 07/17/2020

Leveraging both Lesion Features and Procedural Bias in Neuroimaging: An Dual-Task Split dynamics of inverse scale space

The prediction and selection of lesion features are two important tasks ...

9 Xinwei Sun, et al. ∙

research

∙ 07/14/2020

TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning

Fusing data from multiple modalities provides more information to train ...

18 Xinwei Sun, et al. ∙

research

∙ 07/04/2020

DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths

Over-parameterization is ubiquitous nowadays in training neural networks...

0 Yanwei Fu, et al. ∙

research

∙ 10/14/2019

iSplit LBI: Individualized Partial Ranking with Ties via Split LBI

Due to the inherent uncertainty of data, the problem of predicting parti...

1 Qianqian Xu, et al. ∙

research

∙ 05/23/2019

Parsimonious Deep Learning: A Differential Inclusion Approach with Global Convergence

Over-parameterization is ubiquitous nowadays in training neural networks...

15 Yanwei Fu, et al. ∙

research

∙ 04/24/2019

S^2-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning

This paper proposes a novel Stochastic Split Linearized Bregman Iteratio...

4 Yanwei Fu, et al. ∙

research

∙ 07/29/2018

A Margin-based MLE for Crowdsourced Partial Ranking

A preference order or ranking aggregated from pairwise comparison data i...

0 Qianqian Xu, et al. ∙

research

∙ 07/21/2018

FDR-HS: An Empirical Bayesian Identification of Heterogenous Features in Neuroimage Analysis

Recent studies found that in voxel-based neuroimage analysis, detecting ...

0 Xinwei Sun, et al. ∙

research

∙ 06/12/2018

MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning

It is one typical and general topic of learning a good embedding model t...

0 Bo Zhao, et al. ∙

research

∙ 11/20/2017

Zero-shot Learning via Shared-Reconstruction-Graph Pursuit

Zero-shot learning (ZSL) aims to recognize objects from novel unseen cla...

0 Bo Zhao, et al. ∙

research

∙ 04/16/2017

Boosting with Structural Sparsity: A Differential Inclusion Approach

Boosting as gradient descent algorithms is one popular method in machine...

0 Chendi Huang, et al. ∙

Xinwei Sun

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