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Ivor W. Tsang — most-cited papers & profile · Similarity Search
← authors
·
overview
Ivor W. Tsang
29
papers ·
147
citations ·
68
h-index
Agency for Science, Technology and Research · Nankai University · Institute of High Performance Computing · Institute of Materials Research and Engineering · A*STAR Graduate Academy
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Most-cited papers
Second-order Fine-tuning Without Pain For Llms:a Hessian Informed Zeroth-order Optimizer
2024 · 42 citations
Degeneration in VAE: in the Light of Fisher Information Loss
2018 · 15 citations
Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations
2022 · 14 citations
Intrinsic Reward Driven Imitation Learning via Generative Model
2020 · 12 citations
Collaborative Generative Hashing for Marketing and Fast Cold-start Recommendation
2020 · 12 citations
Deep Pairwise Hashing for Cold-start Recommendation
2020 · 11 citations
A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting
2021 · 10 citations
Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation
2020 · 9 citations
Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
2024 · 5 citations
N-ary Error Correcting Coding Scheme
2016 · 4 citations
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
2023 · 3 citations
Decouple before Align: Visual Disentanglement Enhances Prompt Tuning
2025 · 1 citations
UTSGAN: Unseen Transition Suss GAN for Transition-Aware Image-to-image Translation
2023 · 1 citations
Collaborative Group-Aware Hashing for Fast Recommender Systems
2025
Beyond-expert Performance With Limited Demonstrations: Efficient Imitation Learning With Double Exploration
2025
Top co-authors
and Jingjing Li
· 1
Defu Lian
· 1
Guowu Yang
· 1
Hongzhi Yin
· 1
Joey Tianyi Zhou
· 1
Rick Siow Mong Goh
· 1
Sinno Jialin Pan
· 1
Xinxing Xu
· 1
Yan Zhang
· 1
Zheng Qin
· 1
Topics
cs.IR
cs.LG
stat.ML
cs.AI
Vision-Language Models
Exploration
Linear & Patch Models
Transformer Forecasters
Fine-Tuning
Efficiency