% Key papers for Conditional distribution alignment (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2019_sliced_14841,
  title = {{Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2020_model_15899,
  title = {{Model Adaptation: Unsupervised Domain Adaptation Without Source Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{icml2017_asymmetric_36070,
  title = {{Asymmetric Tri-training for Unsupervised Domain Adaptation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/616},
}

@inproceedings{cvpr2019_transferrable_14945,
  title = {{Transferrable Prototypical Networks for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{icml2018_learning_36801,
  title = {{Learning Semantic Representations for Unsupervised Domain Adaptation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1961},
}

@inproceedings{neurips2020_self_60807,
  title = {{Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16867},
}

@inproceedings{neurips2016_learning_55827,
  title = {{Learning Transferrable Representations for Unsupervised Domain Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6892},
}

@inproceedings{aaai2020_unsupervised_102229,
  title = {{Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6091},
}

@inproceedings{aaai2020_homm_101885,
  title = {{HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5745},
}

@inproceedings{iclr2017_central_28322,
  title = {{Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=SkB-_mcel},
}

@inproceedings{neurips2020_universal_61046,
  title = {{Universal Domain Adaptation through Self Supervision}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17003},
}

@inproceedings{cvpr2019_sequence_14815,
  title = {{Sequence-To-Sequence Domain Adaptation Network for Robust Text Image Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{aaai2017_distant_104449,
  title = {{Distant Domain Transfer Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10826},
}

@inproceedings{aaai2022_active_1702,
  title = {{Active Learning for Domain Adaptation: An Energy-Based Approach}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08708-active-learning-for-domain-adaptation-an-energy-based-approach/},
}

@inproceedings{cvpr2021_domain_16898,
  title = {{Domain Consensus Clustering for Universal Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2017_domain_12222,
  title = {{Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2017_learning_12419,
  title = {{Learning an Invariant Hilbert Space for Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{aaai2018_unsupervised_102525,
  title = {{Unsupervised Domain Adaptation With Distribution Matching Machines}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11792},
}

@inproceedings{aaai2019_transferable_101710,
  title = {{Transferable Curriculum for Weakly-Supervised Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4425},
}

@inproceedings{cvpr2020_unsupervised_16415,
  title = {{Unsupervised Domain Adaptation With Hierarchical Gradient Synchronization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{aaai2025_higda_9425,
  title = {{HiGDA: Hierarchical Graph of Nodes to Learn Local-to-Global Topology for Semi-Supervised Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32662},
}

@inproceedings{aaai2024_agile_5153,
  title = {{Agile Multi-Source-Free Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29272},
}

@inproceedings{aaai2024_cycle_5543,
  title = {{Cycle Self-Refinement for Multi-Source Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29654},
}

@inproceedings{aaai2024_mlnet_6585,
  title = {{MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28182},
}

@inproceedings{aaai2024_a_5064,
  title = {{A Separation and Alignment Framework for Black-Box Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29532},
}

@inproceedings{cvpr2024_unveiling_25175,
  title = {{Unveiling the Unknown: Unleashing the Power of Unknown to Known in Open-Set Source-Free Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29383},
}

@inproceedings{aaai2024_confusing_5471,
  title = {{Confusing Pair Correction Based on Category Prototype for Domain Adaptation under Noisy Environments}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29650},
}

@inproceedings{cvpr2025_multi_27015,
  title = {{Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/35145},
}

@inproceedings{aaai2024_low_6550,
  title = {{Low Category Uncertainty and High Training Potential Instance Learning for Unsupervised Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29630},
}

@inproceedings{aaai2024_reviewing_7146,
  title = {{Reviewing the Forgotten Classes for Domain Adaptation of Black-Box Predictors}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29624},
}

@inproceedings{aaai2026_from_99928,
  title = {{From Blind Transfer to Wise Selection: Prototype-Driven Neighbor-Domain Adaptation for Fake News Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37049},
}

@inproceedings{cvpr2025_distinguish_25971,
  title = {{Distinguish Then Exploit: Source-free Open Set Domain Adaptation via Weight Barcode Estimation and Sparse Label Assignment}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33252},
}

@inproceedings{cvpr2025_modfinity_26877,
  title = {{MODfinity: Unsupervised Domain Adaptation with Multimodal Information Flow Intertwining}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34951},
}

@inproceedings{neurips2024_self_73879,
  title = {{Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93632},
}

@inproceedings{neurips2024_on_74491,
  title = {{On \$f\$-Divergence Principled Domain Adaptation: An Improved Framework}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93084},
}

@inproceedings{cvpr2025_a_25372,
  title = {{A Theory of Learning Unified Model via Knowledge Integration from Label Space Varying Domains}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32405},
}

@inproceedings{icml2025_concept_47752,
  title = {{Concept-Based Unsupervised Domain Adaptation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44848},
}

@inproceedings{neurips2025_domain_77740,
  title = {{Domain Adaptive Hashing Retrieval via VLM Assisted Pseudo-Labeling and Dual Space Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=X7ak8ohJPg},
}

@inproceedings{neurips2025_nemotron_78016,
  title = {{Nemotron-CLIMB: Clustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=aBlqKPkc4a},
}

@inproceedings{neurips2025_rred_78494,
  title = {{RrED: Black-box Unsupervised Domain Adaptation via Rectifying-reasoning Errors of Diffusion}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=fLAoFt2pea},
}