School of Mathematics and Statistics - Research Publications

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Start date: 2020 × Type: Conference Paper × Author: Gong, Mingming ×

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Now showing 1 - 10 of 14
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    Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples
    Xia, X ; Han, B ; Zhan, Y ; Yu, J ; Gong, M ; Gong, C ; Liu, T (IEEE, 2023-01-01)
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    Generating Dynamic Kernels via Transformers for Lane Detection
    Chen, Z ; Liu, Y ; Gong, M ; Du, B ; Qian, G ; Smith-Miles, K (IEEE, 2023-01-01)
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    Knowledge Distillation for Feature Extraction in Underwater VSLAM
    Yang, J ; Gong, M ; Nair, G ; Lee, JH ; Monty, J ; Pu, Y (IEEE, 2023-01-01)
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    Unpaired Image-to-Image Translation with Shortest Path Regularization
    Xie, S ; Xu, Y ; Gong, M ; Zhang, K (IEEE, 2023-06)
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    Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition
    Zhu, A ; Ke, Q ; Gong, M ; Bailey, J (IEEE COMPUTER SOC, 2023)
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    Progressive Video Summarization via Multimodal Self-supervised Learning
    Li, H ; Ke, Q ; Gong, M ; Drummond, T (IEEE COMPUTER SOC, 2023)
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    Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation
    Xu, Y ; Xie, S ; Wu, W ; Zhang, K ; Gong, M ; Batmanghelich, K (IEEE COMPUTER SOC, 2022)
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    Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint
    Guo, J ; Li, J ; Fu, H ; Gong, M ; Zhang, K ; Tao, D (IEEE COMPUTER SOC, 2022)
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    Likelihood-Free Overcomplete ICA and Applications In Causal Discovery
    Chenwei, DING ; Gong, M ; Zhang, K ; Tao, D ; Wallach, H ; Larochelle, H ; Beygelzimer, A ; d'Alche-Buc, F ; Fox, E ; Garnett, R (The Neural Information Processing Systems Foundation, 2020)
    Causal discovery witnessed significant progress over the past decades. In particular, many recent causal discovery methods make use of independent, non-Gaussian noise to achieve identifiability of the causal models. Existence of hidden direct common causes, or confounders, generally makes causal discovery more difficult; whenever they are present, the corresponding causal discovery algorithms can be seen as extensions of overcomplete independent component analysis (OICA). However, existing OICA algorithms usually make strong parametric assumptions on the distribution of independent components, which may be violated on real data, leading to sub-optimal or even wrong solutions. In addition, existing OICA algorithms rely on the Expectation Maximization (EM) procedure that requires computationally expensive inference of the posterior distribution of independent components. To tackle these problems, we present a Likelihood-Free Overcomplete ICA algorithm (LFOICA) that estimates the mixing matrix directly by back-propagation without any explicit assumptions on the density function of independent components. Thanks to its computational efficiency, the proposed method makes a number of causal discovery procedures much more practically feasible. For illustrative purposes, we demonstrate the computational efficiency and efficacy of our method in two causal discovery tasks on both synthetic and real data.
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    Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering
    Huang, B ; Zhang, K ; Xie, P ; Gong, M ; Xing, EP ; Glymour, C ; Wallach, H ; Larochelle, H ; Beygelzimer, A ; d'Alche-Buc, F ; Fox, E ; Garnett, R (The Neural Information Processing Systems Foundation, 2020)
    State-of-the-art approaches to causal discovery usually assume a fixed underlying causal model. However, it is often the case that causal models vary across domains or subjects, due to possibly omitted factors that affect the quantitative causal effects. As a typical example, causal connectivity in the brain network has been reported to vary across individuals, with significant differences across groups of people, such as autistics and typical controls. In this paper, we develop a unified framework for causal discovery and mechanism-based group identification. In particular, we propose a specific and shared causal model (SSCM), which takes into account the variabilities of causal relations across individuals/groups and leverages their commonalities to achieve statistically reliable estimation. The learned SSCM gives the specific causal knowledge for each individual as well as the general trend over the population. In addition, the estimated model directly provides the group information of each individual. Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed method.