Doc2hash: Learning Discrete Latent variables for Documents Retrieval. - Yifei Zhang; Hao Zhu 010 (NAACL-HLT (1), 2019)

Imbalance Robust Softmax for Deep Embeeding Learning. - Hao Zhu 010; Yang Yuan; Guosheng Hu; Xiang Wu; Neil Martin Robertson (ACCV (5), 2020)

Discrete Wasserstein Autoencoders for Document Retrieval. - Yifei Zhang; Hao Zhu 010 (ICASSP, 2020)

Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learning. - Yifei Zhang; Hao Zhu 010 (CoRR, 2020)

REFINE: Random RangE FInder for Network Embedding. - Hao Zhu 010; Piotr Koniusz (CIKM, 2021)

EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph. - Yuxiang Ren; Hao Zhu 010; Jiawei Zhang 001; Peng Dai 001; Liefeng Bo (ICDE, 2021)

Simple Spectral Graph Convolution. - Hao Zhu 010; Piotr Koniusz (ICLR, 2021)

Contrastive Laplacian Eigenmaps. - Hao Zhu 010; Ke Sun 001; Peter Koniusz (NeurIPS, 2021)

Graph Convolutional Network with Generalized Factorized Bilinear Aggregation. - Hao Zhu 010; Piotr Koniusz (CoRR, 2021)

EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot Learning. - Hao Zhu 010; Piotr Koniusz (CVPR, 2022)

COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning. - Yifei Zhang; Hao Zhu 010; Zixing Song; Piotr Koniusz; Irwin King (KDD, 2022)

Generalized Laplacian Eigenmaps. - Hao Zhu 010; Piotr Koniusz (NeurIPS, 2022)

Graph-adaptive Rectified Linear Unit for Graph Neural Networks. - Yifei Zhang; Hao Zhu 010; Ziqiao Meng; Piotr Koniusz; Irwin King (WWW, 2022)

Spectral Feature Augmentation for Graph Contrastive Learning and Beyond. - Yifei Zhang; Hao Zhu 010; Zixing Song; Piotr Koniusz; Irwin King (AAAI, 2023)

Transductive Few-Shot Learning with Prototype-Based Label Propagation by Iterative Graph Refinement. - Hao Zhu 010; Piotr Koniusz (CVPR, 2023)

Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective. - Yifei Zhang; Hao Zhu 010; Yankai Chen 001; Zixing Song; Piotr Koniusz; Irwin King (NeurIPS, 2023)

From Saliency to DINO: Saliency-guided Vision Transformer for Few-shot Keypoint Detection. - Changsheng Lu; Hao Zhu 010; Piotr Koniusz (CoRR, 2023)

Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning. - Yifei Zhang; Hao Zhu 010; Jiahong Liu 001; Piotr Koniusz; Irwin King (CoRR, 2023)

Geometric View of Soft Decorrelation in Self-Supervised Learning. - Yifei Zhang; Hao Zhu 010; Zixing Song; Yankai Chen 001; Xinyu Fu 004; Ziqiao Meng; Piotr Koniusz; Irwin King (KDD, 2024)

Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs. - Yifei Zhang; Hao Zhu 010; Aiwei Liu; Han Yu 001; Piotr Koniusz; Irwin King (CoRR, 2024)

Protein Fitness Landscape: Spectral Graph Theory Perspective. - Hao Zhu 010; Daniel M. Steinberg; Piotr Koniusz (AISTATS, 2025)

pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation. - Yifei Zhang; Hao Zhu 010; Alysa Ziying Tan; Dianzhi Yu; Longtao Huang; Han Yu 001 (CVPR, 2025)

BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning. - Hao Zhu 010; Yifei Zhang; Junhao Dong 001; Piotr Koniusz (CVPR, 2025)

Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learning. - Yifei Zhang; Hao Zhu 010; Menglin Yang 001; Jiahong Liu 001; Rex Ying; Irwin King; Piotr Koniusz (KDD (1), 2025)

Graph Self-Supervised Learning with Learnable Structural and Positional Encodings. - Asiri Wijesinghe; Hao Zhu 010; Piotr Koniusz (WWW, 2025)