Ding, Liang

24 publications

ICLRW 2025 "Short-Length" Adversarial Training Helps LLMs Defend "Long-Length" Jailbreak Attacks: Theoretical and Empirical Evidence Shaopeng Fu, Liang Ding, Di Wang

AAAI 2025 Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models Wenbin Wang, Liang Ding, Minyan Zeng, Xiabin Zhou, Li Shen, Yong Luo, Wei Yu, Dacheng Tao

AAAI 2025 Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning Tianle Xia, Liang Ding, Guojia Wan, Yibing Zhan, Bo Du, Dacheng Tao

NeurIPS 2025 Layer as Puzzle Pieces: Compressing Large Language Models Through Layer Concatenation Fei Wang, Li Shen, Liang Ding, Chao Xue, Ye Liu, Changxing Ding

ICLRW 2025 MMA: Benchmarking Multi-Modal Large Language Model in Ambiguity Contexts Ru Wang, Selena Song, Liang Ding, Mingming Gong, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo

ICML 2025 Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAG Wenbin Wang, Yongcheng Jing, Liang Ding, Yingjie Wang, Li Shen, Yong Luo, Bo Du, Dacheng Tao

NeurIPS 2025 Self-Evolving Pseudo-Rehearsal for Catastrophic Forgetting with Task Similarity in LLMs Jun Wang, Liang Ding, Shuai Wang, Hongyu Li, Yong Luo, Huangxuan Zhao, Han Hu, Bo Du

NeurIPS 2025 Short-Length Adversarial Training Helps LLMs Defend Long-Length Jailbreak Attacks: Theoretical and Empirical Evidence Shaopeng Fu, Liang Ding, Jingfeng Zhang, Di Wang

ICML 2025 The Energy Loss Phenomenon in RLHF: A New Perspective on Mitigating Reward Hacking Yuchun Miao, Sen Zhang, Liang Ding, Yuqi Zhang, Lefei Zhang, Dacheng Tao

TMLR 2025 Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques Shwai He, Daize Dong, Liang Ding, Ang Li

NeurIPS 2024 InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling Yuchun Miao, Sen Zhang, Liang Ding, Rong Bao, Lefei Zhang, Dacheng Tao

AAAI 2024 Multi-Step Denoising Scheduled Sampling: Towards Alleviating Exposure Bias for Diffusion Models Zhiyao Ren, Yibing Zhan, Liang Ding, Gaoang Wang, Chaoyue Wang, Zhongyi Fan, Dacheng Tao

JMLR 2024 Random Smoothing Regularization in Kernel Gradient Descent Learning Liang Ding, Tianyang Hu, Jiahang Jiang, Donghao Li, Wenjia Wang, Yuan Yao

CVPR 2024 Sheared Backpropagation for Fine-Tuning Foundation Models Zhiyuan Yu, Li Shen, Liang Ding, Xinmei Tian, Yixin Chen, Dacheng Tao

ICML 2023 Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape Yan Sun, Li Shen, Shixiang Chen, Liang Ding, Dacheng Tao

ICLR 2023 FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy Yan Sun, Li Shen, Tiansheng Huang, Liang Ding, Dacheng Tao

IJCAI 2023 Gapformer: Graph Transformer with Graph Pooling for Node Classification Chuang Liu, Yibing Zhan, Xueqi Ma, Liang Ding, Dapeng Tao, Jia Wu, Wenbin Hu

AAAI 2023 Improving Simultaneous Machine Translation with Monolingual Data Hexuan Deng, Liang Ding, Xuebo Liu, Meishan Zhang, Dacheng Tao, Min Zhang

WACV 2023 SD-Conv: Towards the Parameter-Efficiency of Dynamic Convolution Shwai He, Chenbo Jiang, Daize Dong, Liang Ding

CVPR 2022 Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, Ling-Yu Duan

JMLR 2022 Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations Haoyuan Chen, Liang Ding, Rui Tuo

ICLR 2021 Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Zhaopeng Tu

ICLR 2021 Understanding and Improving Lexical Choice in Non-Autoregressive Translation Liang Ding, Longyue Wang, Xuebo Liu, Derek F. Wong, Dacheng Tao, Zhaopeng Tu

ICML 2020 Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features Liang Ding, Rui Tuo, Shahin Shahrampour