News

10 Papers Accepted at ICML 2024

Congratulations to the following scientists from A*STAR’s Centre for Frontier AI Research (CFAR) on having their papers accepted at the International Conference on Machine Learning (ICML) 2024:

  • Prof Ivor Tsang, Director and Distinguished Principal Scientist
  • Prof Ong Yew Soon, Chief Artificial Intelligence Scientist and Advisor
  • Dr Li Xiaoli, Senior Principal Scientist
  • Dr Cheston Tan, Senior Principal Scientist
  • Dr Basura Fernando, Principal Scientist
  • Dr Foo Chuan Sheng, Principal Scientist
  • Dr Chen Zhenghua, Senior Scientist
  • Dr Hu Dapeng, Scientist
  • Dr Lyu Yueming, Scientist
  • Dr Emadeldeen Eldele, Scientist
  • Dr Mohamed Ragab, Scientist
  • Mr Ishaan Singh Rawal, Engineer 
Returning for its 41st edition this year, ICML is one of the fastest growing artificial intelligence (AI) conferences renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in the realms of AI, statistics and data science, as well as essential application areas including machine vision, computational biology, speech recognition and robotics. 

List of accepted papers:

  1. Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion
    Ishaan Singh Rawal, Alexander Matyasko, Shantanu Jaiswal, Basura Fernando, Cheston Tan
  2. Diversified Batch Selection for Training Acceleration
    Feng Hong, Yueming Lyu, Jiangchao Yao, Ya Zhang, Ivor W. Tsang, Yanfeng Wang
  3. TSLANet: Rethinking Transformers for Time Series Representation Learning
    Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Xiaoli Li, Min Wu
  4. Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation
    Dapeng Hu, Jian Liang, Xinchao Wang, Chuan-Sheng Foo
  5. Can Gaussian Sketching Converge Faster on a Preconditioned Landscape?
    Yilong Wang, Haishan Ye, Guang Dai, Ivor W. Tsang
  6. Double Variance Reduction: A Smoothing Trick for Composite Optimisation Problems without First-Order Gradient
    Hao Di, Haishan Ye, Yueling Zhang, Xiangyu Chang, Guang Dai, Ivor W. Tsang
  7. Double Stochasticity Gazes Faster: Snap-Shot Decentralised Stochastic Gradient Tracking Methods
    Hao Di, Haishan Ye, Xiangyu Chang, Guang Dai, Ivor W. Tsang
  8. Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation
    W. Liu, X. Zheng, C. Chen, J. Xu, X. Liao, F. Wang, Y. Tan, and Y. S. Ong
  9. Robust and Fine-tuning-free Instance Attribution for Interpretable NLP
    Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Chuan-Sheng Foo, Bryan Kian Hsiang Low
  10. Optimising Complex Machine Learning Systems with Black-box and Differentiable Components
    Zhiliang Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low

Find out more about ICML 2024.