About Me
I am a Postdoctoral Researcher at Australian Institute for Machine Learning (AIML), part of The University of Adelaide, working with Prof. Qinfeng (Javen) Shi. Prior to that, I obtained my Ph.D. in School of Computer Science from Wuhan University in 2019. During my Ph.D. studies, I have spent more than one year as a visiting student at The University of Adelaide, working with Javen.
My current major research topics are about:
building the bridge between causality and machine learning, e.g., causal representation learning, multi domain/modal Learning;
building the bridge between Bayesian learning and deep learning, e.g., Bayesian deep learning and deep Bayesian learning;
inverse problems in various applications, e.g., computer vision, signal processing.
Contact me: liuyuhang@whu.edu.cn; mr.liuyuhang@gmail.com
News:
(Aug 2024) "Rethinking State Disentanglement in Causal Reinforcement Learning", Check out Arxiv, https://arxiv.org/abs/2408.13498
(Aug 2024) "InvariantStock: Learning Invariant Features for Mastering the Shifting Market". accepted to appear at TMLR 2024.
(Aug 2024) "Uncertainty estimation in HDR imaging with Bayesian neural networks". accepted to appear at PR 2024.
(Jul 2024) "CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts". accepted to appear at ECCV 2024.
(Mar 2024) "Identifiable Latent Neural Causal Models". Check out Arxiv, https://arxiv.org/abs/2403.15711.
(Feb 2024) "Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models". Check out Arxiv, https://arxiv.org/abs/2402.06223.
(Jan 2024) "Identifiable Latent Polynomial Causal Models Through the Lens of Change". Finally, accepted to appear at ICLR 2024.
(Oct 2023) Work in progress "Identifiable Latent Polynomial Causal Models Through the Lens of Change". Check out Arxiv, https://arxiv.org/abs/2310.15580.
(Sep 2022) Work in progress "identifiable latent causal content for domain adaptation under latent covariate shift". Check out Arxiv, https://arxiv.org/abs/2208.14161.
(Sep 2022) Work in progress "Identifying Weight-Variant Latent Causal Models". Check out Arxiv, https://arxiv.org/abs/2208.14153.
(Sep 2022) 1 paper "Truncated Matrix Power Iteration for Differentiable DAG Learning" accepted to appear at NeurIPS 2022.
(Mar 2022) 1 paper (Continual Learning with Sparse Neural Networks) accepted to appear at CVPR 2022.
(Jan 2022) Check the article summarizing part of our works using ML to help agriculture innovation.
(Mar 2019) One paper "Bayesian Nonnegative Matrix Factorization With a Truncated Spike-and-slab Prior" is accepted by ICME2019. Code coming soon.
(Feb 2019) One paper "Variational Bayesian Dropout" is accepted by CVPR2019. Project page? (Coming soon.)
(Nov 2018) Work in progress "Variational Bayesian Dropout". Check out Arxiv, https://arxiv.org/pdf/1811.07533.pdf.
(Sep 2018) Code for paper "Deblurring Natural Image Using Super-Gaussian Fields" has been released! Check "Software".
(Sep 2018) Code for paper "Frame-Based Variational Bayesian Learning for Independent or Dependent Source Separation" has been released! Check "Software".
(July 2018) One paper “Deblurring Natural Image Using Super-Gaussian Fields” is accepted by ECCV2018.
(Dec 2017) One paper “Frame-Based Variational Bayesian Learning for Independent or Dependent Source Separation” is accepted by TNNLS .