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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...
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05/09/2023
Causal Discovery with Unobserved Variables: A Proxy Variable Approach
Discovering causal relations from observational data is important. The e...
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05/09/2023
Causal Discovery from Subsampled Time Series with Proxy Variables
Inferring causal structures from time series data is the central interes...
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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...
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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...
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09/15/2022
Generalized Representations Learning for Time Series Classification
Time series classification is an important problem in real world. Due to...
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07/24/2022
A New Causal Decomposition Paradigm towards Health Equity
Causal decomposition has provided a powerful tool to analyze health disp...
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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...
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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...
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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...
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07/05/2021
Causally Invariant Predictor with Shift-Robustness
This paper proposes an invariant causal predictor that is robust to dist...
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03/30/2021
Causal Hidden Markov Model for Time Series Disease Forecasting
We propose a causal hidden Markov model to achieve robust prediction of ...
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03/30/2021
Controlling the False Discovery Rate in Structural Sparsity: Split Knockoffs
Controlling the False Discovery Rate (FDR) in a variable selection proce...
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12/21/2020
Disease Forecast via Progression Learning
Forecasting Parapapillary atrophy (PPA), i.e., a symptom related to most...
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12/19/2020
Identifying Invariant Texture Violation for Robust Deepfake Detection
Existing deepfake detection methods have reported promising in-distribut...
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11/04/2020
Latent Causal Invariant Model
Current supervised learning can learn spurious correlation during the da...
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11/03/2020
Learning Causal Semantic Representation for Out-of-Distribution Prediction
Conventional supervised learning methods, especially deep ones, are foun...
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09/30/2020
Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification
Mammogram benign or malignant classification with only image-level label...
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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 ...
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07/14/2020
TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning
Fusing data from multiple modalities provides more information to train ...
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07/04/2020
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
Over-parameterization is ubiquitous nowadays in training neural networks...
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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...
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05/23/2019
Parsimonious Deep Learning: A Differential Inclusion Approach with Global Convergence
Over-parameterization is ubiquitous nowadays in training neural networks...
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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...
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07/29/2018
A Margin-based MLE for Crowdsourced Partial Ranking
A preference order or ranking aggregated from pairwise comparison data i...
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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 ...
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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...
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11/20/2017
Zero-shot Learning via Shared-Reconstruction-Graph Pursuit
Zero-shot learning (ZSL) aims to recognize objects from novel unseen cla...
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04/16/2017