Efficient multiple hyperparameter learning for log-linear models. - Chuong B. Do; Chuan-Sheng Foo; Andrew Y. Ng (NIPS, 2007)

A max-margin model for efficient simultaneous alignment and folding of RNA sequences. - Chuong B. Do; Chuan-Sheng Foo; Serafim Batzoglou (ISMB, 2008)

Searching for Rising Stars in Bibliography Networks. - Xiaoli Li 001; Chuan Sheng Foo; Kar Leong Tew; See-Kiong Ng (DASFAA, 2009)

Proximal regularization for online and batch learning. - Chuong B. Do; Quoc V. Le; Chuan-Sheng Foo (ICML, 2009)

A majorization-minimization algorithm for (multiple) hyperparameter learning. - Chuan-Sheng Foo; Chuong B. Do; Andrew Y. Ng (ICML, 2009)

Machine learning models for analyzing chromatin and RNA structure data. - Chuan-Sheng Foo (2017)

Adversarially Learned Anomaly Detection. - Houssam Zenati; Manon Romain; Chuan-Sheng Foo; Bruno Lecouat; Vijay Chandrasekhar 001 (ICDM, 2018)

Semi-Supervised Learning With GANs: Revisiting Manifold Regularization. - Bruno Lecouat; Chuan Sheng Foo; Houssam Zenati; Vijay Ramaseshan Chandrasekhar (ICLR (Workshop), 2018)

Efficient GAN-Based Anomaly Detection. - Houssam Zenati; Chuan Sheng Foo; Bruno Lecouat; Gaurav Manek; Vijay Ramaseshan Chandrasekhar (CoRR, 2018)

Mirror descent in saddle-point problems: Going the extra (gradient) mile. - Panayotis Mertikopoulos; Houssam Zenati; Bruno Lecouat; Chuan-Sheng Foo; Vijay Chandrasekhar 001; Georgios Piliouras (CoRR, 2018)

Manifold regularization with GANs for semi-supervised learning. - Bruno Lecouat; Chuan Sheng Foo; Houssam Zenati; Vijay Chandrasekhar 001 (CoRR, 2018)

Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials. - Léo Laugier; Daniil Bash; Jose Recatala; Hong Kuan Ng; Savitha Ramasamy; Chuan-Sheng Foo; Vijay Ramaseshan Chandrasekhar; Kedar Hippalgaonkar (CoRR, 2018)

Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images. - Bruno Lecouat; Ken Chang; Chuan-Sheng Foo; Balagopal Unnikrishnan; James M. Brown 001; Houssam Zenati; Andrew Beers; Vijay Chandrasekhar 001; Jayashree Kalpathy-Cramer; Pavitra Krishnaswamy (CoRR, 2018)

MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors. - Lile Cai; Bin Zhao; Zhe Wang 019; Jie Lin 001; Chuan Sheng Foo; Mohamed M. Sabry Aly; Vijay Chandrasekhar 001 (CVPR, 2019)

Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile. - Panayotis Mertikopoulos; Bruno Lecouat; Houssam Zenati; Chuan-Sheng Foo; Vijay Chandrasekhar 001; Georgios Piliouras (ICLR (Poster), 2019)

The Unusual Effectiveness of Averaging in GAN Training. - Yasin Yazici; Chuan-Sheng Foo; Stefan Winkler 001; Kim-Hui Yap; Georgios Piliouras; Vijay Chandrasekhar 001 (ICLR (Poster), 2019)

Semi-Supervised Audio Classification with Consistency-Based Regularization. - Kangkang Lu 001; Chuan-Sheng Foo; Kah Kuan Teh; Huy Dat Tran; Vijay Ramaseshan Chandrasekhar (INTERSPEECH, 2019)

TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks. - Lile Cai; Anne-Maelle Barneche; Arthur Herbout; Chuan Sheng Foo; Jie Lin 001; Vijay Ramaseshan Chandrasekhar; Mohamed M. Sabry Aly (ISLPED, 2019)

Towards Practical Unsupervised Anomaly Detection on Retinal Images. - Khalil Ouardini; Huijuan Yang; Balagopal Unnikrishnan; Manon Romain; Camille Garcin; Houssam Zenati; J. Peter Campbell; Michael F. Chiang; Jayashree Kalpathy-Cramer; Vijay Chandrasekhar 001; Pavitra Krishnaswamy; Chuan-Sheng Foo (DART/MIL3ID@MICCAI, 2019)

Learning of Multi-Dimensional Analog Circuits Through Generative Adversarial Network (GAN). - Rahul Dutta; Salahuddin Raju; Ashish James; Leo John Chemmanda; Yong-Joon Jeon; Balagopal Unnikrishnan; Chuan Sheng Foo; Zeng Zeng; Kevin Tshun Chuan Chai; Vijay Ramaseshan Chandrasekhar (SoCC, 2019)

Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions. - Yasin Yazici; Bruno Lecouat; Chuan-Sheng Foo; Stefan Winkler 001; Kim-Hui Yap; Georgios Piliouras; Vijay Chandrasekhar 001 (CoRR, 2019)

Learning to Impute: A General Framework for Semi-supervised Learning. - Wei-Hong Li; Chuan-Sheng Foo; Hakan Bilen (CoRR, 2019)

Mahalanobis Distance Based Adversarial Network for Anomaly Detection. - Yubo Hou; Zhenghua Chen; Min Wu 008; Chuan-Sheng Foo; Xiaoli Li 001; Raed M. Shubair (ICASSP, 2020)

Empirical Analysis Of Overfitting And Mode Drop In Gan Training. - Yasin Yazici; Chuan-Sheng Foo; Stefan Winkler 001; Kim-Hui Yap; Vijay Chandrasekhar 001 (ICIP, 2020)

Semi-supervised Classification of Diagnostic Radiographs with NoTeacher: A Teacher that is Not Mean. - Balagopal Unnikrishnan; Cuong Manh Nguyen; Shafa Balaram; Chuan Sheng Foo; Pavitra Krishnaswamy (MICCAI (1), 2020)

Classification Representations Can be Reused for Downstream Generations. - Saisubramaniam Gopalakrishnan; Pranshu Ranjan Singh; Yasin Yazici; Chuan-Sheng Foo; Vijay Chandrasekhar 001; Arulmurugan Ambikapathi (CoRR, 2020)

Holistic Multi-Modal Memory Network for Movie Question Answering. - Anran Wang 001; Anh Tuan Luu; Chuan-Sheng Foo; Hongyuan Zhu; Yi Tay; Vijay Chandrasekhar 001 (IEEE Trans. Image Process., 2020)

On Automatic Data Augmentation for 3D Point Cloud Classification. - Wanyue Zhang; Xun Xu 002; Fayao Liu; Le Zhang 001; Chuan Sheng Foo (BMVC, 2021)

Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs. - Lile Cai; Xun Xu 002; Jun Hao Liew; Chuan Sheng Foo (CVPR, 2021)

ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity. - Kangkang Lu 001; Cuong Manh Nguyen; Xun Xu 002; Kiran Chari; Yu Jing Goh; Chuan-Sheng Foo (ICLR, 2021)

Special Symbol Attacks On NLP Systems. - Brian Formento; See-Kiong Ng; Chuan-Sheng Foo (IJCNN, 2021)

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning. - Xinyi Xu; Lingjuan Lyu; Xingjun Ma; Chenglin Miao; Chuan Sheng Foo; Bryan Kian Hsiang Low (NeurIPS, 2021)

Validation Free and Replication Robust Volume-based Data Valuation. - Xinyi Xu; Zhaoxuan Wu; Chuan Sheng Foo; Bryan Kian Hsiang Low (NeurIPS, 2021)

A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition. - Govind Narasimman; Kangkang Lu 001; Arun Raja; Chuan Sheng Foo; Mohamed M. Sabry Aly; Jie Lin 001; Vijay Chandrasekhar 001 (CoRR, 2021)

Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach. - Xian Shi; Xun Xu 002; Ke Chen 004; Lile Cai; Chuan Sheng Foo; Kui Jia (CoRR, 2021)

Point Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds. - Fayao Liu; Guosheng Lin; Chuan-Sheng Foo (CoRR, 2021)

An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series. - Astha Garg; Wenyu Zhang 003; Jules Samaran; Savitha Ramasamy; Chuan-Sheng Foo (CoRR, 2021)

Semi-supervised classification of radiology images with NoTeacher: A teacher that is not mean. - Balagopal Unnikrishnan; Cuong Manh Nguyen; Shafa Balaram; Chao Li; Chuan Sheng Foo; Pavitra Krishnaswamy (Medical Image Anal., 2021)

Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction. - Mohamed Ragab 002; Zhenghua Chen; Min Wu 008; Chuan Sheng Foo; Chee Keong Kwoh 001; Ruqiang Yan 001; Xiaoli Li 001 (IEEE Trans. Ind. Informatics, 2021)

Exploring Spatial Diversity for Region-Based Active Learning. - Lile Cai; Xun Xu 002; Lining Zhang; Chuan-Sheng Foo (IEEE Trans. Image Process., 2021)

Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis. - Fayao Liu; Guosheng Lin; Chuan-Sheng Foo; Chaitanya K. Joshi; Jie Lin 001 (3DV, 2022)

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. - Sebastian Shenghong Tay; Xinyi Xu; Chuan Sheng Foo; Bryan Kian Hsiang Low (AAAI, 2022)

A Minimally Supervised Approach for Medical Image Quality Assessment in Domain Shift Settings. - Huijuan Yang; Aaron S. Coyner; Feri Guretno; Ivan Ho Mien; Chuan Sheng Foo; J. Peter Campbell; Susan Ostmo; Michael F. Chiang; Pavitra Krishnaswamy (ICASSP, 2022)

Exploring Active Learning for Semiconductor Defect Segmentation. - Lile Cai; Ramanpreet Singh Pahwa; Xun Xu 002; Jie Wang 042; Richard Chang 002; Lining Zhang; Chuan-Sheng Foo (ICIP, 2022)

Mixed Membership Generative Adversarial Networks. - Yasin Yazici; Bruno Lecouat; Kim-Hui Yap; Stefan Winkler 001; Georgios Piliouras; Vijay Chandrasekhar 001; Chuan-Sheng Foo (ICIP, 2022)

Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity. - Sebastian Shenghong Tay; Chuan Sheng Foo; Daisuke Urano; Richalynn Leong; Bryan Kian Hsiang Low (ICML, 2022)

Domain Generalization via Selective Consistency Regularization for Time Series Classification. - Wenyu Zhang 003; Mohamed Ragab 002; Chuan-Sheng Foo (ICPR, 2022)

Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding. - Xian Shi; Xun Xu 002; Wanyue Zhang; Xiatian Zhu; Chuan Sheng Foo; Kui Jia (ICPR, 2022)

Few-Shot Adaptation of Pre-Trained Networks for Domain Shift. - Wenyu Zhang 003; Li Shen; Wanyue Zhang; Chuan-Sheng Foo (IJCAI, 2022)

Bayesian Deep Active Learning for Analog Circuit Performance Classification. - Lining Zhang; Salahuddin Raju; Ashish James; Rahul Dutta; Gregoire Fournier; Damien Lancry; Kevin Chai Tshun Chuan; Vijay Ramaseshan Chandrasekhar; Chuan Sheng Foo (ISCAS, 2022)

Automated Deep Learning Platform for Accelerated Analog Circuit Design. - Rahul Dutta; Ashish James; Salahuddin Raju; Yong-Joon Jeon; Chuan Sheng Foo; Kevin Tshun Chuan Chai (SOCC, 2022)

Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime. - Xun Xu 002; Jingyi Liao; Lile Cai; Cuong Manh Nguyen; Kangkang Lu 001; Wanyue Zhang; Yasin Yazici; Chuan Sheng Foo (CoRR, 2022)

SemiCurv: Semi-Supervised Curvilinear Structure Segmentation. - Xun Xu 002; Manh Cuong Nguyen; Yasin Yazici; Kangkang Lu 001; Hlaing Min; Chuan-Sheng Foo (CoRR, 2022)

Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning. - Manas Gupta; Efe Camci; Vishandi Rudy Keneta; Abhishek Vaidyanathan; Ritwik Kanodia; Chuan-Sheng Foo; Min Wu 008; Jie Lin 001 (CoRR, 2022)

Co-Learning with Pre-Trained Networks Improves Source-Free Domain Adaptation. - Wenyu Zhang 003; Li Shen; Chuan-Sheng Foo (CoRR, 2022)

Classify and generate: Using classification latent space representations for image generations. - Saisubramaniam Gopalakrishnan; Pranshu Ranjan Singh; Yasin Yazici; Chuan-Sheng Foo; Vijay Chandrasekhar 001; Arulmurugan Ambikapathi (Neurocomputing, 2022)

Scalable and Practical Natural Gradient for Large-Scale Deep Learning. - Kazuki Osawa; Yohei Tsuji; Yuichiro Ueno; Akira Naruse; Chuan-Sheng Foo; Rio Yokota (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

MA-GANet: A Multi-Attention Generative Adversarial Network for Defocus Blur Detection. - Zeyu Jiang; Xun Xu 002; Le Zhang 001; Chao Zhang 072; Chuan Sheng Foo; Ce Zhu (IEEE Trans. Image Process., 2022)

SemiCurv: Semi-Supervised Curvilinear Structure Segmentation. - Xun Xu 002; Cuong Manh Nguyen; Yasin Yazici; Kangkang Lu 001; Hlaing Min; Chuan-Sheng Foo (IEEE Trans. Image Process., 2022)

Fourier Sensitivity and Regularization of Computer Vision Models. - Kiran Krishnamachari; See-Kiong Ng; Chuan-Sheng Foo (Trans. Mach. Learn. Res., 2022)

An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series. - Astha Garg; Wenyu Zhang 003; Jules Samaran; Ramasamy Savitha; Chuan-Sheng Foo (IEEE Trans. Neural Networks Learn. Syst., 2022)

Probably Approximate Shapley Fairness with Applications in Machine Learning. - Zijian Zhou; Xinyi Xu; Rachael Hwee Ling Sim; Chuan Sheng Foo; Bryan Kian Hsiang Low (AAAI, 2023)

No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities. - Sebastian Shenghong Tay; Quoc Phong Nguyen; Chuan Sheng Foo; Bryan Kian Hsiang Low (AISTATS, 2023)

FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery. - Xinyi Xu; Zhaoxuan Wu; Arun Verma; Chuan Sheng Foo; Bryan Kian Hsiang Low (AISTATS, 2023)

Using Punctuation as an Adversarial Attack on Deep Learning-Based NLP Systems: An Empirical Study. - Brian Formento; Chuan-Sheng Foo; Anh Tuan Luu; See-Kiong Ng (EACL (Findings), 2023)

On Adversarial Robustness of Audio Classifiers. - Kangkang Lu 001; Manh Cuong Nguyen; Xun Xu 002; Chuan Sheng Foo (ICASSP, 2023)

Rethinking the Role of Pre-Trained Networks in Source-Free Domain Adaptation. - Wenyu Zhang 003; Li Shen; Chuan-Sheng Foo (ICCV, 2023)

Fair yet Asymptotically Equal Collaborative Learning. - Xiaoqiang Lin; Xinyi Xu; See-Kiong Ng; Chuan-Sheng Foo; Bryan Kian Hsiang Low (ICML, 2023)

Source-Free Domain Adaptation with Temporal Imputation for Time Series Data. - Mohamed Ragab 002; Emadeldeen Eldele; Min Wu 008; Chuan-Sheng Foo; Xiaoli Li 001; Zhenghua Chen (KDD, 2023)

Bayesian Optimization with Cost-varying Variable Subsets. - Sebastian Tay; Chuan Sheng Foo; Daisuke Urano; Richalynn Leong; Bryan Kian Hsiang Low (NeurIPS, 2023)

Model Shapley: Equitable Model Valuation with Black-box Access. - Xinyi Xu; Thanh Lam; Chuan Sheng Foo; Bryan Kian Hsiang Low (NeurIPS, 2023)

PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation. - Dapeng Hu; Jian Liang 001; Xinchao Wang; Chuan-Sheng Foo (CoRR, 2023)

ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data. - Mohamed Ragab 002; Emadeldeen Eldele; Wee Ling Tan; Chuan-Sheng Foo; Zhenghua Chen; Min Wu 008; Chee Keong Kwoh 001; Xiaoli Li 001 (ACM Trans. Knowl. Discov. Data, 2023)

Mitigating Real-World Distribution Shifts in the Fourier Domain. - Kiran Krishnamachari; See-Kiong Ng; Chuan-Sheng Foo (Trans. Mach. Learn. Res., 2023)

Diverse and consistent multi-view networks for semi-supervised regression. - Cuong Manh Nguyen; Arun Raja; Le Zhang 001; Xun Xu 002; Balagopal Unnikrishnan; Mohamed Ragab 002; Kangkang Lu 001; Chuan-Sheng Foo (Mach. Learn., 2023)

Fine Structure-Aware Sampling: A New Sampling Training Scheme for Pixel-Aligned Implicit Models in Single-View Human Reconstruction. - Kennard Yanting Chan; Fayao Liu; Guosheng Lin; Chuan Sheng Foo; Weisi Lin (AAAI, 2024)

Towards Safe and Efficient Analog Circuit Design: Active Learning for Feasibility Region Exploration. - Sezin Kircali Ata; Zhi-Hui Kong; Anusha James; Lile Cai; Kiat Seng Yeo; Khin Mi Mi Aung; Chuan Sheng Foo; Ashish James (APCCAS, 2024)

Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias. - Wenyu Zhang 003; Qingmu Liu; Felix Ong Wei Cong; Mohamed Ragab 002; Chuan-Sheng Foo (CVPR, 2024)

R-Cyclic Diffuser: Reductive and Cyclic Latent Diffusion for 3D Clothed Human Digitalization. - Kennard Yanting Chan; Fayao Liu; Guosheng Lin; Chuan Sheng Foo; Weisi Lin (CVPR, 2024)

Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior. - Cheng Chen; Xiaofeng Yang; Fan Yang 103; Chengzeng Feng; Zhoujie Fu; Chuan-Sheng Foo; Guosheng Lin; Fayao Liu (CVPR, 2024)

REACTO: Reconstructing Articulated Objects from a Single Video. - Chaoyue Song; Jiacheng Wei; Chuan Sheng Foo; Guosheng Lin; Fayao Liu (CVPR, 2024)

3DFG-PIFu: 3D Feature Grids for Human Digitization from Sparse Views. - Kennard Yanting Chan; Fayao Liu; Guosheng Lin; Chuan Sheng Foo; Weisi Lin (ECCV (24), 2024)

Waterfall: Scalable Framework for Robust Text Watermarking and Provenance for LLMs. - Gregory Kang Ruey Lau; Xinyuan Niu 001; Hieu Dao; Jiangwei Chen 001; Chuan-Sheng Foo; Bryan Kian Hsiang Low (EMNLP, 2024)

Box-Level Class-Balanced Sampling For Active Object Detection. - Jingyi Liao; Xun Xu 002; Chuan-Sheng Foo; Lile Cai (ICIP, 2024)

A Unified Framework for Bayesian Optimization under Contextual Uncertainty. - Sebastian Shenghong Tay; Chuan-Sheng Foo; Daisuke Urano; Richalynn Leong; Bryan Kian Hsiang Low (ICLR, 2024)

Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components. - Zhiliang Chen; Chuan-Sheng Foo; Bryan Kian Hsiang Low (ICML, 2024)

Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation. - Dapeng Hu; Jian Liang 001; Xinchao Wang; Chuan-Sheng Foo (ICML, 2024)

Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions. - Jingtan Wang 001; Xiaoqiang Lin; Rui Qiao 006; Chuan-Sheng Foo; Bryan Kian Hsiang Low (ICML, 2024)

The Initialization Factor: Understanding its Impact on Active Learning for Analog Circuit Design. - Sezin Kircali Ata; Zhi-Hui Kong; Anusha James; Lile Cai; Kiat Seng Yeo; Khin Mi Mi Aung; Chuan Sheng Foo; Ashish James (ISCAS, 2024)

SemRoDe: Macro Adversarial Training to Learn Representations that are Robust to Word-Level Attacks. - Brian Formento; Wenjie Feng 001; Chuan-Sheng Foo; Anh Tuan Luu; See-Kiong Ng (NAACL-HLT, 2024)

Towards Reliable Model Selection for Unsupervised Domain Adaptation: An Empirical Study and A Certified Baseline. - Dapeng Hu; Romy Luo; Jian Liang 001; Chuan Sheng Foo (NeurIPS, 2024)

Data Distribution Valuation. - Xinyi Xu; Shuaiqi Wang; Chuan Sheng Foo; Bryan Kian Hsiang Low; Giulia Fanti (NeurIPS, 2024)

PromptAD: Zero-shot Anomaly Detection using Text Prompts. - Yiting Li; Adam David Goodge; Fayao Liu; Chuan-Sheng Foo (WACV, 2024)

Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation. - Mohamed Ragab 002; Peiliang Gong; Emadeldeen Eldele; Wenyu Zhang 003; Min Wu 008; Chuan-Sheng Foo; Daoqiang Zhang; Xiaoli Li 001; Zhenghua Chen (CoRR, 2024)

Waterfall: Framework for Robust and Scalable Text Watermarking. - Gregory Kang Ruey Lau; Xinyuan Niu 001; Hieu Dao; Jiangwei Chen 001; Chuan-Sheng Foo; Bryan Kian Hsiang Low (CoRR, 2024)

Training neural networks with classification rules for incorporating domain knowledge. - Wenyu Zhang 003; Fayao Liu; Cuong Manh Nguyen; Zhong Liang Ou Yang; Savitha Ramasamy; Chuan-Sheng Foo (Knowl. Based Syst., 2024)

COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection. - Jingyi Liao; Xun Xu 002; Manh Cuong Nguyen; Adam Goodge; Chuan Sheng Foo (IEEE Trans. Image Process., 2024)

When low-vision task meets dense prediction tasks with less data: an auxiliary self-trained geometry regularization. - Zaiwang Gu; Weide Liu; Xulei Yang; Chuan-Sheng Foo; Jun Cheng 003 (Trans. Mach. Learn. Res., 2024)

Hybrid Active Learning with Uncertainty-Weighted Embeddings. - Yinan He; Lile Cai; Jingyi Liao; Chuan-Sheng Foo (Trans. Mach. Learn. Res., 2024)

Uniformly Distributed Feature Representations for Fair and Robust Learning. - Kiran Krishnamachari; See-Kiong Ng; Chuan-Sheng Foo (Trans. Mach. Learn. Res., 2024)

Label-Efficient Point Cloud Semantic Segmentation: A Holistic Active Learning Approach. - Xian Shi; Lile Cai; Ke Chen 018; Chuan Sheng Foo; Kui Jia; Xun Xu 002 (World Sci. Annu. Rev. Artif. Intell., 2024)

Revisiting pretraining for semi-supervised learning in the low-label regime. - Xun Xu 002; Jingyi Liao; Lile Cai; Manh Cuong Nguyen; Kangkang Lu 001; Wanyue Zhang; Yasin Yazici; Chuan Sheng Foo (Neurocomputing, 2024)

Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation. - Weide Liu; Zhonghua Wu; Yang Zhao; Yuming Fang; Chuan-Sheng Foo; Jun Cheng 003; Guosheng Lin (Int. J. Comput. Vis., 2024)

On Representation Knowledge Distillation for Graph Neural Networks. - Chaitanya K. Joshi; Fayao Liu; Xu Xun; Jie Lin 001; Chuan Sheng Foo (IEEE Trans. Neural Networks Learn. Syst., 2024)

WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data. - Xinyang Lu; Jingtan Wang 001; Zitong Zhao; Zhongxiang Dai; Chuan-Sheng Foo; See-Kiong Ng; Bryan Kian Hsiang Low (ACL (Findings), 2025)

CADCrafter: Generating Computer-Aided Design Models from Unconstrained Images. - Cheng Chen; Jiacheng Wei; Tianrun Chen; Chi Zhang 007; Xiaofeng Yang; Shangzhan Zhang; Bingchen Yang; Chuan-Sheng Foo; Guosheng Lin; Qixing Huang; Fayao Liu (CVPR, 2025)

Evidential Learning-based Certainty Estimation for Robust Dense Feature Matching. - Lile Cai; Chuan-Sheng Foo; Xun Xu 002; Zaiwang Gu; Jun Cheng 003; Xulei Yang (ICLR, 2025)

Robust-PIFu: Robust Pixel-aligned Implicit Function for 3D Human Digitalization from a Single Image. - Kennard Yanting Chan; Fayao Liu; Guosheng Lin; Chuan-Sheng Foo; Weisi Lin (ICLR, 2025)

Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs with Semantic Space. - Zhiliang Chen; Xinyuan Niu 001; Chuan-Sheng Foo; Bryan Kian Hsiang Low (ICLR, 2025)

Confidence Elicitation: A New Attack Vector for Large Language Models. - Brian Formento; Chuan-Sheng Foo; See-Kiong Ng (ICLR, 2025)

On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning. - Yongyi Su; Yushu Li; Nanqing Liu; Kui Jia; Xulei Yang; Chuan-Sheng Foo; Xun Xu 002 (ICLR, 2025)

DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks. - Zhiliang Chen; Gregory Kang Ruey Lau; Chuan-Sheng Foo; Bryan Kian Hsiang Low (CoRR, 2025)

Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-training. - Wenyu Zhang 003; Li Shen; Chuan-Sheng Foo (Int. J. Comput. Vis., 2025)

Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs using Semantic Space. - Zhiliang Chen; Xinyuan Niu 001; Chuan-Sheng Foo; Bryan Kian Hsiang Low (CoRR, 2025)

Mining Semantic Correlations Between Mispredictions and Corrections for Interactive Semantic Segmentation. - Yutong Gao 001; Congyan Lang; Fayao Liu; Chuan-Sheng Foo; Yuanzhouhan Cao; Lijuan Sun; Yunchao Wei (IEEE Trans. Neural Networks Learn. Syst., 2025)

WaterDrum: Watermarking for Data-centric Unlearning Metric. - Xinyang Lu; Xinyuan Niu 001; Gregory Kang Ruey Lau; Bui Thi Cam Nhung; Rachael Hwee Ling Sim; Fanyu Wen; Chuan-Sheng Foo; See-Kiong Ng; Bryan Kian Hsiang Low (CoRR, 2025)

MoDA: Modeling Deformable 3D Objects from Casual Videos. - Chaoyue Song; Jiacheng Wei; Tianyi Chen; Yiwen Chen; Chuan-Sheng Foo; Fayao Liu; Guosheng Lin (Int. J. Comput. Vis., 2025)