An improved method of using icesat altimetry data to extract Tibetan Plateau glacier thickness change rate. - Tianjin Huang; Li Jia 001; Jing Lu; Jie Zhou 003 (IGARSS, 2016)

A New Method to Estimate Changes in Glacier Surface Elevation Based on Polynomial Fitting of Sparse ICESat - GLAS Footprints. - Tianjin Huang; Li Jia 001; Massimo Menenti; Jing Lu; Jie Zhou 003; Guangcheng Hu (Sensors, 2017)

Bridging the Performance Gap between FGSM and PGD Adversarial Training. - Tianjin Huang; Vlado Menkovski; Yulong Pei; Mykola Pechenizkiy (CoRR, 2020)

calibrated adversarial training. - Tianjin Huang; Vlado Menkovski; Yulong Pei; Mykola Pechenizkiy (ACML, 2021)

On Generalization of Graph Autoencoders with Adversarial Training. - Tianjin Huang; Yulong Pei; Vlado Menkovski; Mykola Pechenizkiy (ECML/PKDD (2), 2021)

You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. - Tianjin Huang; Tianlong Chen 001; Meng Fang; Vlado Menkovski; Jiaxu Zhao 002; Lu Yin 006; Yulong Pei; Decebal Constantin Mocanu; Zhangyang Wang; Mykola Pechenizkiy; Shiwei Liu 003 (LoG, 2022)

Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks. - Tianjin Huang; Yulong Pei; Vlado Menkovski; Mykola Pechenizkiy (ECML/PKDD (1), 2022)

Superposing many tickets into one: A performance booster for sparse neural network training. - Lu Yin 006; Vlado Menkovski; Meng Fang; Tianjin Huang; Yulong Pei; Mykola Pechenizkiy (UAI, 2022)

Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training. - Lu Yin 006; Vlado Menkovski; Meng Fang; Tianjin Huang; Yulong Pei; Mykola Pechenizkiy; Decebal Constantin Mocanu; Shiwei Liu 003 (CoRR, 2022)

Direction-aggregated Attack for Transferable Adversarial Examples. - Tianjin Huang; Vlado Menkovski; Yulong Pei; Yuhao Wang; Mykola Pechenizkiy (ACM J. Emerg. Technol. Comput. Syst., 2022)

ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks. - Yulong Pei; Tianjin Huang; Werner van Ipenburg; Mykola Pechenizkiy (Mach. Learn., 2022)

Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. - Lu Yin 006; Shiwei Liu 003; Meng Fang; Tianjin Huang; Vlado Menkovski; Mykola Pechenizkiy (AAAI, 2023)

Heterophily-Based Graph Neural Network for Imbalanced Classification. - Zirui Liang; Yuntao Li; Tianjin Huang; Akrati Saxena; Yulong Pei; Mykola Pechenizkiy (COMPLEX NETWORKS (1), 2023)

Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! - Shiwei Liu 003; Tianlong Chen 001; Zhenyu Zhang 015; Xuxi Chen; Tianjin Huang; Ajay Kumar Jaiswal; Zhangyang Wang (ICLR, 2023)

Are Large Kernels Better Teachers than Transformers for ConvNets? - Tianjin Huang; Lu Yin 006; Zhenyu Zhang 015; Li Shen 008; Meng Fang; Mykola Pechenizkiy; Zhangyang Wang; Shiwei Liu 003 (ICML, 2023)

Dynamic Sparsity Is Channel-Level Sparsity Learner. - Lu Yin 006; Gen Li 012; Meng Fang; Li Shen 008; Tianjin Huang; Zhangyang Wang; Vlado Menkovski; Xiaolong Ma; Mykola Pechenizkiy; Shiwei Liu 003 (NeurIPS, 2023)

Enhancing Adversarial Training via Reweighting Optimization Trajectory. - Tianjin Huang; Shiwei Liu 003; Tianlong Chen 001; Meng Fang; Li Shen 008; Vlado Menkovski; Lu Yin 006; Yulong Pei; Mykola Pechenizkiy (ECML/PKDD (1), 2023)

The Counterattack of CNNs in Self-Supervised Learning: Larger Kernel Size might be All You Need. - Tianjin Huang; Tianlong Chen 001; Zhangyang Wang; Shiwei Liu 003 (CoRR, 2023)

Are Sparse Neural Networks Better Hard Sample Learners? - Qiao Xiao; Boqian Wu; Lu Yin 006; Christopher Neil Gadzinski; Tianjin Huang; Mykola Pechenizkiy; Decebal Constantin Mocanu (BMVC, 2024)

(PASS) Visual Prompt Locates Good Structure Sparsity through a Recurrent HyperNetwork. - Tianjin Huang; Meng Fang; Li Shen 008; Fan Liu; Yulong Pei; Mykola Pechenizkiy; Shiwei Liu 003; Tianlong Chen 001 (CoRR, 2024)

RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction. - Yutian Liu 001; Soora Rasouli; Melvin Wong; Tao Feng 008; Tianjin Huang (Inf. Fusion, 2024)

Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective. - Can Jin; Tianjin Huang; Yihua Zhang; Mykola Pechenizkiy; Sijia Liu 001; Shiwei Liu 003; Tianlong Chen 001 (AAAI, 2025)

SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training. - Tianjin Huang; Ziquan Zhu; Gaojie Jin; Lu Liu; Zhangyang Wang; Shiwei Liu 003 (ICLR, 2025)

Enhancing Robust Fairness via Confusional Spectral Regularization. - Gaojie Jin; Sihao Wu; Jiaxu Liu 001; Tianjin Huang; Ronghui Mu (ICLR, 2025)

Composable Interventions for Language Models. - Arinbjörn Kolbeinsson; Kyle O'Brien; Tianjin Huang; Shanghua Gao; Shiwei Liu 003; Jonathan Richard Schwarz; Anurag Jayant Vaidya; Faisal Mahmood 001; Marinka Zitnik; Tianlong Chen 001; Thomas Hartvigsen (ICLR, 2025)

Traffic congestion predictor: A spatiotemporal graph neural network with congestion-conditional adaptive mechanism and optimization algorithm. - Yutian Liu 001; Jian Gong; Tianjin Huang; Jinde Cao (Expert Syst. Appl., 2025)

FS-GNN: Improving Fairness in Graph Neural Networks via Joint Sparsification. - Jiaxu Zhao 002; Tianjin Huang; Shiwei Liu 003; Jie Yin 001; Yulong Pei; Meng Fang; Mykola Pechenizkiy (Neurocomputing, 2025)

Few-Shot Oriented Object Detection in Remote Sensing Images via Memorable Contrastive Learning. - Jiawei Zhou; Wuzhou Li; Yi Cao; Hongtao Cai; Tianjin Huang; Gui-Song Xia; Xiang Li 046 (IEEE Trans. Geosci. Remote. Sens., 2025)

Stable-SPAM: How to Train in 4-Bit More Stably than 16-Bit Adam. - Tianjin Huang; Haotian Hu; Zhenyu Zhang 015; Gaojie Jin; Xiang Li 001; Li Shen 008; Tianlong Chen 001; Lu Liu; Qingsong Wen; Zhangyang Wang; Shiwei Liu 003 (CoRR, 2025)

Principal Eigenvalue Regularization for Improved Worst-Class Certified Robustness of Smoothed Classifiers. - Gaojie Jin; Tianjin Huang; Ronghui Mu; Xiaowei Huang 001 (CoRR, 2025)

REOBench: Benchmarking Robustness of Earth Observation Foundation Models. - Xiang Li 001; Yong Tao; Siyuan Zhang; Siwei Liu 001; Zhitong Xiong; Chunbo Luo; Lu Liu; Mykola Pechenizkiy; Xiao Xiang Zhu 001; Tianjin Huang (CoRR, 2025)

LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning. - Zihang Liu; Tianyu Pang; Oleg Balabanov; Chaoqun Yang; Tianjin Huang; Lu Yin 006; Yaoqing Yang; Shiwei Liu 003 (CoRR, 2025)

The Compositional Architecture of Regret in Large Language Models. - Xiangxiang Cui; Shu Yang 010; Tianjin Huang; Wanyu Lin; Lijie Hu; Di Wang 015 (CoRR, 2025)

Infinite Sampling: Efficient and Stable Grouped RL Training for Large Language Models. - Liangyu Wang; Huanyi Xie; Xinhai Wang; Tianjin Huang; Mengdi Li; Di Wang 015 (CoRR, 2025)

LOST: Low-rank and Sparse Pre-training for Large Language Models. - Jiaxi Li; Lu Yin 006; Li Shen 008; Jinjin Xu; Liwu Xu; Tianjin Huang; Wenwu Wang 001; Shiwei Liu 003; Xilu Wang 001 (CoRR, 2025)