Mao, Yongyi

46 publications

UAI 2025 Adversarial Training May Induce Deteriorating Distributions Runzhi Tian, Yongyi Mao

ICLR 2025 Algorithmic Stability Based Generalization Bounds for Adversarial Training Runzhi Tian, Yongyi Mao

ICLRW 2025 Distributional Information Embedding: A Framework for Multi-Bit Watermarking Haiyun He, Yepeng Liu, Ziqiao Wang, Yongyi Mao, Yuheng Bu

ICML 2025 Generalization in Federated Learning: A Conditional Mutual Information Framework Ziqiao Wang, Cheng Long, Yongyi Mao

NeurIPS 2025 Theoretically Grounded Framework for LLM Watermarking: A Distribution-Adaptive Approach Haiyun He, Yepeng Liu, Ziqiao Wang, Yongyi Mao, Yuheng Bu

ICLRW 2025 Theoretically Grounded Framework for LLM Watermarking: A Distribution-Adaptive Approach Haiyun He, Yepeng Liu, Ziqiao Wang, Yongyi Mao, Yuheng Bu

NeurIPS 2024 Generalization Bounds via Conditional $f$-Information Ziqiao Wang, Yongyi Mao

AAAI 2024 Narrowing the Gap Between Supervised and Unsupervised Sentence Representation Learning with Large Language Model Mingxin Li, Richong Zhang, Zhijie Nie, Yongyi Mao

NeurIPS 2024 On $f$-Divergence Principled Domain Adaptation: An Improved Framework Ziqiao Wang, Yongyi Mao

AAAI 2024 On Unsupervised Domain Adaptation: Pseudo Label Guided Mixup for Adversarial Prompt Tuning Fanshuang Kong, Richong Zhang, Ziqiao Wang, Yongyi Mao

UAI 2024 Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States Ziqiao Wang, Yongyi Mao

AAAI 2023 Adversarial Word Dilution as Text Data Augmentation in Low-Resource Regime Junfan Chen, Richong Zhang, Zheyan Luo, Chunming Hu, Yongyi Mao

ICLR 2023 Information-Theoretic Analysis of Unsupervised Domain Adaptation Ziqiao Wang, Yongyi Mao

AAAI 2023 Interpolating Graph Pair to Regularize Graph Classification Hongyu Guo, Yongyi Mao

AAAI 2023 Multi-Mask Label Mapping for Prompt-Based Learning Jirui Qi, Richong Zhang, Jaein Kim, Junfan Chen, Wenyi Qin, Yongyi Mao

NeurIPSW 2023 On Robust Overfitting: Adversarial Training Induced Distribution Matters Runzhi Tian, Yongyi Mao

ICLR 2023 On the Inadequacy of Optimizing Alignment and Uniformity in Contrastive Learning of Sentence Representations Zhijie Nie, Richong Zhang, Yongyi Mao

ICLR 2023 Over-Training with Mixup May Hurt Generalization Zixuan Liu, Ziqiao Wang, Hongyu Guo, Yongyi Mao

NeurIPS 2023 Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds Ziqiao Wang, Yongyi Mao

ICML 2023 Tighter Information-Theoretic Generalization Bounds from Supersamples Ziqiao Wang, Yongyi Mao

NeurIPSW 2023 Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States Ziqiao Wang, Yongyi Mao

AAAI 2022 ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification Junfan Chen, Richong Zhang, Yongyi Mao, Jie Xu

ICLR 2022 On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications Ziqiao Wang, Yongyi Mao

NeurIPSW 2022 Over-Training with Mixup May Hurt Generalization Zixuan Liu, Ziqiao Wang, Hongyu Guo, Yongyi Mao

AAAI 2022 Unsupervised Sentence Representation via Contrastive Learning with Mixing Negatives Yanzhao Zhang, Richong Zhang, Samuel Mensah, Xudong Liu, Yongyi Mao

IJCAI 2021 Hierarchical Modeling of Label Dependency and Label Noise in Fine-Grained Entity Typing Junshuang Wu, Richong Zhang, Yongyi Mao, Masoumeh Soflaei Shahrbabak, Jinpeng Huai

AAAI 2021 On Scalar Embedding of Relative Positions in Attention Models Junshuang Wu, Richong Zhang, Yongyi Mao, Junfan Chen

ICLR 2021 On the Dynamics of Training Attention Models Haoye Lu, Yongyi Mao, Amiya Nayak

AAAI 2021 On the SoftMax Bottleneck of Recurrent Language Models Dwarak Govind Parthiban, Yongyi Mao, Diana Inkpen

AAAI 2021 Progressive Multi-Task Learning with Controlled Information Flow for Joint Entity and Relation Extraction Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, Xudong Liu

CVPR 2021 Regularizing Neural Networks via Adversarial Model Perturbation Yaowei Zheng, Richong Zhang, Yongyi Mao

IJCAI 2021 Robust Regularization with Adversarial Labelling of Perturbed Samples Xiaohui Guo, Richong Zhang, Yaowei Zheng, Yongyi Mao

AAAI 2020 Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers Masoumeh Soflaei, Hongyu Guo, Ali Al-Bashabsheh, Yongyi Mao, Richong Zhang

ICLR 2020 MixUp as Directional Adversarial Training Guillaume P. Archambault, Yongyi Mao, Hongyu Guo, Richong Zhang

AAAI 2020 Relation Extraction with Convolutional Network over Learnable Syntax-Transport Graph Kai Sun, Richong Zhang, Yongyi Mao, Samuel Mensah, Xudong Liu

AAAI 2020 Replicate, Walk, and Stop on Syntax: An Effective Neural Network Model for Aspect-Level Sentiment Classification Yaowei Zheng, Richong Zhang, Samuel Mensah, Yongyi Mao

AAAI 2019 Generating Chinese Ci with Designated Metrical Structure Richong Zhang, Xinyu Liu, Xinwei Chen, Zhiyuan Hu, Zhaoqing Xu, Yongyi Mao

AAAI 2019 LENA: Locality-Expanded Neural Embedding for Knowledge Base Completion Fanshuang Kong, Richong Zhang, Yongyi Mao, Ting Deng

AAAI 2019 MixUp as Locally Linear Out-of-Manifold Regularization Hongyu Guo, Yongyi Mao, Richong Zhang

IJCAI 2019 Modeling Noisy Hierarchical Types in Fine-Grained Entity Typing: A Content-Based Weighting Approach Junshuang Wu, Richong Zhang, Yongyi Mao, Hongyu Guo, Jinpeng Huai

AAAI 2018 Embedding of Hierarchically Typed Knowledge Bases Richong Zhang, Fanshuang Kong, Chenyue Wang, Yongyi Mao

UAI 2016 On Hyper-Parameter Estimation in Empirical Bayes: A Revisit of the MacKay Algorithm Chune Li, Yongyi Mao, Richong Zhang, Jinpeng Huai

IJCAI 2016 On the Representation and Embedding of Knowledge Bases Beyond Binary Relations Jianfeng Wen, Jianxin Li, Yongyi Mao, Shini Chen, Richong Zhang

AAAI 2014 A Model for Aggregating Contributions of Synergistic Crowdsourcing Workflows Yili Fang, Hailong Sun, Richong Zhang, Jinpeng Huai, Yongyi Mao

IJCAI 2011 Recommender Systems from "Words of Few Mouths" Richong Zhang, Thomas T. Tran, Yongyi Mao

UAI 2004 Convolutional Factor Graphs as Probabilistic Models Yongyi Mao, Frank R. Kschischang, Brendan J. Frey