Jiacheng Zhu

 
            @misc{liang2025modomodo,
              title={MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning},
              author={Yiqing Liang and Jielin Qiu and Wenhao Ding and Zuxin Liu and James Tompkin and Mengdi Xu and Mengzhou Xia and Zhengzhong Tu and Laixi Shi and Jiacheng Zhu},
              year={2025},
              eprint={2505.24871},
              archivePrefix={arXiv},
              primaryClass={cs.LG}
            }
          

 
            @misc{brelgabrielsson2024compress,
              title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
              author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
              year={2025},
              eprint={2407.00066},
              archivePrefix={arXiv},
              primaryClass={cs.DC}
            }
          

 
            @inproceedings{tam2024llm,
              title={LLM Merging: Building LLMs Efficiently through Merging},
              author={Derek Tam and Margaret Li and Prateek Yadav and Rickard Brøel Gabrielsson and Jiacheng Zhu and Kristjan Greenewald and Mikhail Yurochkin and Mohit Bansal and Colin Raffel and Leshem Choshen},
              booktitle={Advances in Neural Information Processing Systems},
              year={2024}
            }
          

 
            @inproceedings{qiu2024mmsum,
              title={MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos},
              author={Qiu, Jielin and Zhu, Jiacheng and Han, William and Kumar, Aditesh and Mittal, Karthik and Jin, Claire and Yang, Zhengyuan and Li, Linjie and Wang, Jianfeng and Zhao, Ding and others},
              booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
              pages={21909--21921},
              year={2024}
            }
          

 
            @article{zhu2024asymmetry,
              title={Asymmetry in Low-Rank Adapters of Foundation Models}, 
              author={Jiacheng Zhu and Kristjan Greenewald and Kimia Nadjahi and Haitz Sáez de Ocáriz Borde and Rickard Brüel Gabrielsson and Leshem Choshen and Marzyeh Ghassemi and Mikhail Yurochkin and Justin Solomon},
              year={2024},
            }
          

 
            @article{zhu2024functional,
                title={Functional optimal transport: map estimation and domain adaptation for functional data},
                author={Jiacheng Zhu and Aritra Guha and Dat Do and Mengdi Xu and XuanLong Nguyen and Ding Zhao},
                journal={Journal of Machine Learning Research},
                year={2024}
            }
          

 
            @InProceedings{pmlr-v202-zhu23i,
                title = 	 {Interpolation for Robust Learning: Data Augmentation on {W}asserstein Geodesics},
                author =       {Zhu, Jiacheng and Qiu, Jielin and Guha, Aritra and Yang, Zhuolin and Nguyen, Xuanlong and Li, Bo and Zhao, Ding},
                booktitle = 	 {Proceedings of the 40th International Conference on Machine Learning},
                pages = 	 {43129--43157},
                year = 	 {2023},
                editor = 	 {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan},
                volume = 	 {202},
                series = 	 {Proceedings of Machine Learning Research},
                month = 	 {23--29 Jul},
                publisher =    {PMLR},
                pdf = 	 {https://proceedings.mlr.press/v202/zhu23i/zhu23i.pdf},
                url = 	 {https://proceedings.mlr.press/v202/zhu23i.html},
                abstract = 	 {We propose to study and promote the robustness of a model as per its performance on a continuous geodesic interpolation of subpopulations, e.g., a class of samples in a classification problem. Specifically, (1) we augment the data by finding the worst-case Wasserstein barycenter on the geodesic connecting subpopulation distributions. (2) we regularize the model for smoother performance on the continuous geodesic path connecting subpopulation distributions. (3) Additionally, we provide a theoretical guarantee of robustness improvement and investigate how the geodesic location and the sample size contribute, respectively. Experimental validations of the proposed strategy on four datasets including CIFAR-100 and ImageNet, establish the efficacy of our method, e.g., our method improves the baselines' certifiable robustness on CIFAR10 upto 7.7%, with 16.8% on empirical robustness on CIFAR-100. Our work provides a new perspective of model robustness through the lens of Wasserstein geodesic-based interpolation with a practical off-the-shelf strategy that can be combined with existing robust training methods.}
            }
          

 
            @inproceedings{
                huang2022curriculum,
                title={Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation},
                author={Peide Huang and Mengdi Xu and Jiacheng Zhu and Laixi Shi and Fei Fang and Ding Zhao},
                booktitle={Advances in Neural Information Processing Systems},
                editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
                year={2022},
                url={https://openreview.net/forum?id=_cFdPHRLuJ}
                }
          

 
            @article{xu2020task,
                title={Task-agnostic online reinforcement learning with an infinite mixture of gaussian processes},
                author={Xu, Mengdi and Ding, Wenhao and Zhu, Jiacheng and Liu, Zuxin and Chen, Baiming and Zhao, Ding},
                journal={Advances in Neural Information Processing Systems},
                volume={33},
                pages={6429--6440},
                year={2020}
              }