Yuhang Liu

Orcid: 0000-0002-8195-9349

Affiliations:
  • University of Adelaide, Australian Institute for Machine Learning, Adelaide, Australia
  • Wuhan University, Computer School, Wuhan, China (2014 - 2019)


According to our database1, Yuhang Liu authored at least 27 papers between 2018 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

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Bibliography

2025
Causal Disentanglement and Cross-Modal Alignment for Enhanced Few-Shot Learning.
CoRR, August, 2025

Interpreting Chest X-rays Like a Radiologist: A Benchmark with Clinical Reasoning.
CoRR, May, 2025

Negate or Embrace: On How Misalignment Shapes Multimodal Representation Learning.
CoRR, April, 2025

I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data?
CoRR, March, 2025

Seeing Beyond Labels: Source-Free Domain Adaptation via Hypothesis Consolidation of Prediction Rationale.
Trans. Mach. Learn. Res., 2025

Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation.
Trans. Mach. Learn. Res., 2025

Coreset Selection via Reducible Loss in Continual Learning.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Analytic DAG Constraints for Differentiable DAG Learning.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024
InvariantStock: Learning Invariant Features for Mastering the Shifting Market.
Trans. Mach. Learn. Res., 2024

Uncertainty estimation in HDR imaging with Bayesian neural networks.
Pattern Recognit., 2024

Rethinking State Disentanglement in Causal Reinforcement Learning.
CoRR, 2024

Identifiable Latent Neural Causal Models.
CoRR, 2024

Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models.
CoRR, 2024

Identifiable Latent Polynomial Causal Models through the Lens of Change.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

CLAP: Isolating Content from Style Through Contrastive Learning with Augmented Prompts.
Proceedings of the Computer Vision - ECCV 2024, 2024

2023
CLAP: Contrastive Learning with Augmented Prompts for Robustness on Pretrained Vision-Language Models.
CoRR, 2023

2022
Identifying Latent Causal Content for Multi-Source Domain Adaptation.
CoRR, 2022

Weight-variant Latent Causal Models.
CoRR, 2022

Truncated Matrix Power Iteration for Differentiable DAG Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Lightweight Network for High Dynamic Range Imaging.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022


2019
Bayesian Nonnegative Matrix Factorization with a Truncated Spike-and-Slab Prior.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2019

Variational Bayesian Dropout With a Hierarchical Prior.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Frame-Based Variational Bayesian Learning for Independent or Dependent Source Separation.
IEEE Trans. Neural Networks Learn. Syst., 2018

Variational Bayesian Dropout.
CoRR, 2018

Deblurring Natural Image Using Super-Gaussian Fields.
Proceedings of the Computer Vision - ECCV 2018, 2018


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