Bryan Kian Hsiang Low

194 papers A* 82A 13B 2Journal 92Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	BarrierSteer: LLM Safety via Learning Barrier Steering. CoRR Thanh Q. Tran, Arun Verma, Kiwan Wong, Bryan Kian Hsiang Low, Daniela Rus, Wei Xiao
2026	J	`jnl`	Diffusion-Inspired Reconfiguration of Transformers for Uncertainty Calibration. CoRR Manh Cuong Dao, Quang Hung Pham, Phi Le Nguyen, Truong Thao Nguyen, Bryan Kian Hsiang Low, Trong Nghia Hoang
2026	J	`jnl`	MineDraft: A Framework for Batch Parallel Speculative Decoding. CoRR Zhenwei Tang, Arun Verma, Zijian Zhou, Zhaoxuan Wu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low
2026	J	`jnl`	PC-MoE: memory-efficient and privacy-preserving collaborative training for Mixture-of-Experts LLMs. Mach. Learn. Ze Yu Zhang, Bolin Ding, Bryan Kian Hsiang Low
2026	—	`conf`	Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graph for Retrieval-Augmented Generation. EACL (Volume 1: Long Papers) Ze Yu Zhang, Zitao Li, Yaliang Li, Bolin Ding, Bryan Kian Hsiang Low
2026	J	`jnl`	The Chicken and Egg Dilemma: Co-optimizing Data and Model Configurations for LLMs. CoRR Zhiliang Chen, Alfred Wei Lun Leong, Shao Yong Ong, Apivich Hemachandra, Gregory Kang Ruey Lau, Chuan-Sheng Foo, Zhengyuan Liu, Nancy F. Chen, Bryan Kian Hsiang Low
2026	J	`jnl`	TreeGrad-Ranker: Feature Ranking via O(L)-Time Gradients for Decision Trees. CoRR Weida Li, Yaoliang Yu, Bryan Kian Hsiang Low
2026	J	`jnl`	Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume. CoRR Gregory Kang Ruey Lau, Hieu Dao, Nicole Kan Hui Lin, Bryan Kian Hsiang Low
2025	J	`jnl`	Active Human Feedback Collection via Neural Contextual Dueling Bandits. CoRR Arun Verma, Xiaoqiang Lin, Zhongxiang Dai, Daniela Rus, Bryan Kian Hsiang Low
2025	J	`jnl`	ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment. CoRR Xiaoqiang Lin, Arun Verma, Zhongxiang Dai, Daniela Rus, See-Kiong Ng, Bryan Kian Hsiang Low
2025	J	`jnl`	Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization. J. Mach. Learn. Res. Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng
2025	A*	`conf`	BILBO: BILevel Bayesian Optimization. ICML Wan Theng Ruth Chew, Quoc Phong Nguyen, Bryan Kian Hsiang Low
2025	J	`jnl`	BILBO: BILevel Bayesian Optimization. CoRR Wan Theng Ruth Chew, Quoc Phong Nguyen, Bryan Kian Hsiang Low
2025	J	`jnl`	Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs using Semantic Space. CoRR Zhiliang Chen, Xinyuan Niu, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2025	A*	`conf`	Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs with Semantic Space. ICLR Zhiliang Chen, Xinyuan Niu, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2025	J	`jnl`	COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents. CoRR Arun Verma, Indrajit Saha, Makoto Yokoo, Bryan Kian Hsiang Low
2025	J	`jnl`	DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks. CoRR Zhiliang Chen, Gregory Kang Ruey Lau, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2025	A	`conf`	DUPRE: Data Utility Prediction for Efficient Data Valuation. AAMAS Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim, Quoc Phong Nguyen, See-Kiong Ng, Bryan Kian Hsiang Low
2025	J	`jnl`	DUPRE: Data Utility Prediction for Efficient Data Valuation. CoRR Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim, Quoc Phong Nguyen, See-Kiong Ng, Bryan Kian Hsiang Low
2025	A*	`conf`	Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning Tasks. EMNLP Wenyang Hu, Gregory Kang Ruey Lau, Diwen Liu, Jizhuo Chen, See-Kiong Ng, Bryan Kian Hsiang Low
2025	A*	`conf`	Efficient Top-m Data Values Identification for Data Selection. ICLR Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Bryan Kian Hsiang Low
2025	A*	`conf`	Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models. ICML Yao Shu, Wenyang Hu, See-Kiong Ng, Bryan Kian Hsiang Low, Fei Richard Yu
2025	A*	`conf`	Group-robust Sample Reweighting for Subpopulation Shifts via Influence Functions. ICLR Rui Qiao, Zhaoxuan Wu, Jingtan Wang, Pang Wei Koh, Bryan Kian Hsiang Low
2025	J	`jnl`	Group-robust Sample Reweighting for Subpopulation Shifts via Influence Functions. CoRR Rui Qiao, Zhaoxuan Wu, Jingtan Wang, Pang Wei Koh, Bryan Kian Hsiang Low
2025	J	`jnl`	Incentivizing Time-Aware Fairness in Data Sharing. CoRR Jiangwei Chen, Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim, Arun Verma, Zhaoxuan Wu, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2025	J	`jnl`	MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents. CoRR Zijian Zhou, Ao Qu, Zhaoxuan Wu, Sunghwan Kim, Alok Prakash, Daniela Rus, Jinhua Zhao, Bryan Kian Hsiang Low, Paul Pu Liang
2025	A*	`conf`	NICE Data Selection for Instruction Tuning in LLMs with Non-differentiable Evaluation Metric. ICML Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Pang Wei Koh, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2025	A*	`conf`	Neural Dueling Bandits: Preference-Based Optimization with Human Feedback. ICLR Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low
2025	J	`jnl`	PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs. CoRR Ze Yu Zhang, Bolin Ding, Bryan Kian Hsiang Low
2025	A*	`conf`	PIED: Physics-Informed Experimental Design for Inverse Problems. ICLR Apivich Hemachandra, Gregory Kang Ruey Lau, See-Kiong Ng, Bryan Kian Hsiang Low
2025	J	`jnl`	PIED: Physics-Informed Experimental Design for Inverse Problems. CoRR Apivich Hemachandra, Gregory Kang Ruey Lau, See-Kiong Ng, Bryan Kian Hsiang Low
2025	A*	`conf`	Paid with Models: Optimal Contract Design for Collaborative Machine Learning. AAAI Bingchen Wang, Zhaoxuan Wu, Fusheng Liu, Bryan Kian Hsiang Low
2025	J	`jnl`	Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble. CoRR Bingchen Wang, Zi-Yu Khoo, Bryan Kian Hsiang Low
2025	J	`jnl`	REFRAG: Rethinking RAG based Decoding. CoRR Xiaoqiang Lin, Aritra Ghosh, Bryan Kian Hsiang Low, Anshumali Shrivastava, Vijai Mohan
2025	J	`jnl`	ReasonIR: Training Retrievers for Reasoning Tasks. CoRR Rulin Shao, Rui Qiao, Varsha Kishore, Niklas Muennighoff, Xi Victoria Lin, Daniela Rus, Bryan Kian Hsiang Low, Sewon Min, Wen-tau Yih, Pang Wei Koh, Luke Zettlemoyer
2025	J	`jnl`	Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation. CoRR Ze Yu Zhang, Zitao Li, Yaliang Li, Bolin Ding, Bryan Kian Hsiang Low
2025	—	`conf`	TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding. ACL (1) Zhaoxuan Wu, Zijian Zhou, Arun Verma, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low
2025	J	`jnl`	TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding. CoRR Zhaoxuan Wu, Zijian Zhou, Arun Verma, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low
2025	—	`conf`	Uncovering Scaling Laws for Large Language Models via Inverse Problems. EMNLP (Findings) Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
2025	J	`jnl`	Uncovering Scaling Laws for Large Language Models via Inverse Problems. CoRR Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
2025	—	`conf`	WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data. ACL (Findings) Xinyang Lu, Jingtan Wang, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low
2025	J	`jnl`	WaterDrum: Watermarking for Data-centric Unlearning Metric. CoRR Xinyang Lu, Xinyuan Niu, Gregory Kang Ruey Lau, Bui Thi Cam Nhung, Rachael Hwee Ling Sim, Fanyu Wen, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low
2024	A*	`conf`	A Unified Framework for Bayesian Optimization under Contextual Uncertainty. ICLR Sebastian Shenghong Tay, Chuan-Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low
2024	A*	`conf`	Active Set Ordering. NeurIPS Quoc Phong Nguyen, Sunil Gupta, Svetha Venkatesh, Bryan Kian Hsiang Low, Patrick Jaillet
2024	A*	`conf`	DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning. NeurIPS Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low
2024	J	`jnl`	DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning. CoRR Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low
2024	A*	`conf`	Data Distribution Valuation. NeurIPS Xinyi Xu, Shuaiqi Wang, Chuan Sheng Foo, Bryan Kian Hsiang Low, Giulia Fanti
2024	J	`jnl`	Data Distribution Valuation. CoRR Xinyi Xu, Shuaiqi Wang, Chuan-Sheng Foo, Bryan Kian Hsiang Low, Giulia Fanti
2024	J	`jnl`	Data value estimation on private gradients. CoRR Zijian Zhou, Xinyi Xu, Daniela Rus, Bryan Kian Hsiang Low
2024	J	`jnl`	Data-Centric AI in the Age of Large Language Models. CoRR Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low
2024	A*	`conf`	DeRDaVa: Deletion-Robust Data Valuation for Machine Learning. AAAI Xiao Tian, Rachael Hwee Ling Sim, Jue Fan, Bryan Kian Hsiang Low
2024	A*	`conf`	Decentralized Sum-of-Nonconvex Optimization. AAAI Zhuanghua Liu, Bryan Kian Hsiang Low
2024	J	`jnl`	Decentralized Sum-of-Nonconvex Optimization. CoRR Zhuanghua Liu, Bryan Kian Hsiang Low
2024	A*	`conf`	Deletion-Anticipative Data Selection with a Limited Budget. ICML Rachael Hwee Ling Sim, Jue Fan, Xiao Tian, Patrick Jaillet, Bryan Kian Hsiang Low
2024	J	`jnl`	Dependency Structure Search Bayesian Optimization for Decision Making Models. Trans. Mach. Learn. Res. Mohit Rajpal, Lac Gia Tran, Yehong Zhang, Bryan Kian Hsiang Low
2024	J	`jnl`	Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks. CoRR Gregory Kang Ruey Lau, Wenyang Hu, Diwen Liu, Jizhuo Chen, See-Kiong Ng, Bryan Kian Hsiang Low
2024	A*	`conf`	Distributionally Robust Data Valuation. ICML Xiaoqiang Lin, Xinyi Xu, Zhaoxuan Wu, See-Kiong Ng, Bryan Kian Hsiang Low
2024	J	`jnl`	Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models. CoRR Yao Shu, Wenyang Hu, See-Kiong Ng, Bryan Kian Hsiang Low, Fei Richard Yu
2024	J	`jnl`	Global-to-Local Support Spectrums for Language Model Explainability. CoRR Lucas Agussurja, Xinyang Lu, Bryan Kian Hsiang Low
2024	A*	`conf`	Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization. NeurIPS Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
2024	A*	`conf`	Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions. ICML Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2024	J	`jnl`	Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions. CoRR Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2024	A*	`conf`	Incentive-Aware Federated Learning with Training-Time Model Rewards. ICLR Zhaoxuan Wu, Mohammad Mohammadi Amiri, Ramesh Raskar, Bryan Kian Hsiang Low
2024	J	`jnl`	Incentives in Private Collaborative Machine Learning. CoRR Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet
2024	A*	`conf`	Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. AAAI Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
2024	J	`jnl`	Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. CoRR Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
2024	A*	`conf`	Localized Zeroth-Order Prompt Optimization. NeurIPS Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, See-Kiong Ng, Bryan Kian Hsiang Low
2024	J	`jnl`	Localized Zeroth-Order Prompt Optimization. CoRR Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu, Xiangqiang Lin, Zhongxiang Dai, See-Kiong Ng, Bryan Kian Hsiang Low
2024	A*	`conf`	Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes. ICLR Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
2024	J	`jnl`	Neural Dueling Bandits. CoRR Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low
2024	J	`jnl`	On Newton's Method to Unlearn Neural Networks. CoRR Nhung Bui, Xinyang Lu, See-Kiong Ng, Bryan Kian Hsiang Low
2024	J	`jnl`	Online Fair Division with Contextual Bandits. CoRR Arun Verma, Indrajit Saha, Makoto Yokoo, Bryan Kian Hsiang Low
2024	A*	`conf`	Optimistic Bayesian Optimization with Unknown Constraints. ICLR Quoc Phong Nguyen, Wan Theng Ruth Chew, Le Song, Bryan Kian Hsiang Low, Patrick Jaillet
2024	A*	`conf`	PINNACLE: PINN Adaptive ColLocation and Experimental points selection. ICLR Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
2024	J	`jnl`	PINNACLE: PINN Adaptive ColLocation and Experimental points selection. CoRR Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
2024	J	`jnl`	Paid with Models: Optimal Contract Design for Collaborative Machine Learning. CoRR Bingchen Wang, Zhaoxuan Wu, Fusheng Liu, Bryan Kian Hsiang Low
2024	—	`conf`	Position Paper: Data-Centric AI in the Age of Large Language Models. EMNLP (Findings) Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low
2024	A*	`conf`	Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars. NeurIPS Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
2024	J	`jnl`	Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars. CoRR Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
2024	J	`jnl`	Prompt Optimization with Human Feedback. CoRR Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
2024	A*	`conf`	Robustifying and Boosting Training-Free Neural Architecture Search. ICLR Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low
2024	J	`jnl`	Robustifying and Boosting Training-Free Neural Architecture Search. CoRR Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low
2024	J	`jnl`	Self-Interested Agents in Collaborative Learning: An Incentivized Adaptive Data-Centric Framework. CoRR Nithia Vijayan, Bryan Kian Hsiang Low
2024	J	`jnl`	TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs. CoRR Cheng Wang, Xinyang Lu, See-Kiong Ng, Bryan Kian Hsiang Low
2024	A*	`conf`	Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components. ICML Zhiliang Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2024	A*	`conf`	Understanding Domain Generalization: A Noise Robustness Perspective. ICLR Rui Qiao, Bryan Kian Hsiang Low
2024	J	`jnl`	Understanding Domain Generalization: A Noise Robustness Perspective. CoRR Rui Qiao, Bryan Kian Hsiang Low
2024	J	`jnl`	Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models. CoRR Ze Yu Zhang, Arun Verma, Finale Doshi-Velez, Bryan Kian Hsiang Low
2024	A*	`conf`	Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers. ICML Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
2024	J	`jnl`	Waterfall: Framework for Robust and Scalable Text Watermarking. CoRR Gregory Kang Ruey Lau, Xinyuan Niu, Hieu Dao, Jiangwei Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2024	A*	`conf`	Waterfall: Scalable Framework for Robust Text Watermarking and Provenance for LLMs. EMNLP Gregory Kang Ruey Lau, Xinyuan Niu, Hieu Dao, Jiangwei Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2024	A*	`conf`	Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization. ICML Zhuanghua Liu, Cheng Chen, Luo Luo, Bryan Kian Hsiang Low
2023	A*	`conf`	Batch Bayesian Optimization For Replicable Experimental Design. NeurIPS Zhongxiang Dai, Quoc Phong Nguyen, Sebastian Tay, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low, Patrick Jaillet
2023	J	`jnl`	Batch Bayesian Optimization for Replicable Experimental Design. CoRR Zhongxiang Dai, Quoc Phong Nguyen, Sebastian Shenghong Tay, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low, Patrick Jaillet
2023	A*	`conf`	Bayesian Optimization with Cost-varying Variable Subsets. NeurIPS Sebastian Tay, Chuan Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low
2023	A*	`conf`	Collaborative Causal Inference with Fair Incentives. ICML Rui Qiao, Xinyi Xu, Bryan Kian Hsiang Low
2023	J	`jnl`	DeRDaVa: Deletion-Robust Data Valuation for Machine Learning. CoRR Xiao Tian, Rachael Hwee Ling Sim, Jue Fan, Bryan Kian Hsiang Low
2023	A*	`conf`	Exploiting Correlated Auxiliary Feedback in Parameterized Bandits. NeurIPS Arun Verma, Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low
2023	J	`jnl`	Exploiting Correlated Auxiliary Feedback in Parameterized Bandits. CoRR Arun Verma, Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low
2023	A	`conf`	FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery. AISTATS Xinyi Xu, Zhaoxuan Wu, Arun Verma, Chuan Sheng Foo, Bryan Kian Hsiang Low
2023	A*	`conf`	Fair yet Asymptotically Equal Collaborative Learning. ICML Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2023	J	`jnl`	Fair yet Asymptotically Equal Collaborative Learning. CoRR Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2023	A	`conf`	FedHQL: Federated Heterogeneous Q-Learning. AAMAS Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low
2023	J	`jnl`	FedHQL: Federated Heterogeneous Q-Learning. CoRR Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low, Roger Wattenhofer
2023	A*	`conf`	Federated Neural Bandits. ICLR Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet
2023	J	`jnl`	Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients. CoRR Yao Shu, Xiaoqiang Lin, Zhongxiang Dai, Bryan Kian Hsiang Low
2023	J	`jnl`	Fine-tuning Language Models with Generative Adversarial Feedback. CoRR Ze Yu Zhang, Lau Jia Jaw, Zhang Hui, Bryan Kian Hsiang Low
2023	J	`jnl`	Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks. CoRR Tiedong Liu, Bryan Kian Hsiang Low
2023	J	`jnl`	Hessian-Aware Bayesian Optimization for Decision Making Systems. CoRR Mohit Rajpal, Lac Gia Tran, Yehong Zhang, Bryan Kian Hsiang Low
2023	A*	`conf`	Incentives in Private Collaborative Machine Learning. NeurIPS Rachael Hwee Ling Sim, Yehong Zhang, Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet
2023	A*	`conf`	Model Shapley: Equitable Model Valuation with Black-box Access. NeurIPS Xinyi Xu, Thanh Lam, Chuan Sheng Foo, Bryan Kian Hsiang Low
2023	A	`conf`	No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities. AISTATS Sebastian Shenghong Tay, Quoc Phong Nguyen, Chuan Sheng Foo, Bryan Kian Hsiang Low
2023	A*	`conf`	Probably Approximate Shapley Fairness with Applications in Machine Learning. AAAI Zijian Zhou, Xinyi Xu, Rachael Hwee Ling Sim, Chuan Sheng Foo, Bryan Kian Hsiang Low
2023	J	`jnl`	Pruning during training by network efficacy modeling. Mach. Learn. Mohit Rajpal, Yehong Zhang, Bryan Kian Hsiang Low
2023	A*	`conf`	Quantum Bayesian Optimization. NeurIPS Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
2023	J	`jnl`	Quantum Bayesian Optimization. CoRR Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
2023	J	`jnl`	Recursive reasoning-based training-time adversarial machine learning. Artif. Intell. Yizhou Chen, Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Teck-Hua Ho
2023	A*	`conf`	Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation. ICLR Thanh Lam, Arun Verma, Bryan Kian Hsiang Low, Patrick Jaillet
2023	A*	`conf`	Training-Free Neural Active Learning with Initialization-Robustness Guarantees. ICML Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low
2023	J	`jnl`	Training-Free Neural Active Learning with Initialization-Robustness Guarantees. CoRR Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low
2023	J	`jnl`	Use Your INSTINCT: INSTruction optimization usIng Neural bandits Coupled with Transformers. CoRR Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
2023	J	`jnl`	WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data. CoRR Jingtan Wang, Xinyang Lu, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low
2023	A*	`conf`	Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation. ICLR Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet, Bryan Kian Hsiang Low
2022	J	`jnl`	Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization. CoRR Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng
2022	A*	`conf`	Bayesian Optimization under Stochastic Delayed Feedback. ICML Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low
2022	J	`jnl`	Bayesian Optimization under Stochastic Delayed Feedback. CoRR Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low
2022	A*	`conf`	DAVINZ: Data Valuation using Deep Neural Networks at Initialization. ICML Zhaoxuan Wu, Yao Shu, Bryan Kian Hsiang Low
2022	A*	`conf`	Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges. IJCAI Rachael Hwee Ling Sim, Xinyi Xu, Bryan Kian Hsiang Low
2022	A*	`conf`	Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity. ICML Sebastian Shenghong Tay, Chuan Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low
2022	J	`jnl`	Federated Neural Bandit. CoRR Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet
2022	A*	`conf`	Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. AAAI Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low
2022	A	`conf`	Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten. AsiaCCS Quoc Phong Nguyen, Ryutaro Oikawa, Dinil Mon Divakaran, Mun Choon Chan, Bryan Kian Hsiang Low
2022	J	`jnl`	Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten. CoRR Quoc Phong Nguyen, Ryutaro Oikawa, Dinil Mon Divakaran, Mun Choon Chan, Bryan Kian Hsiang Low
2022	A*	`conf`	NASI: Label- and Data-agnostic Neural Architecture Search at Initialization. ICLR Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, Bryan Kian Hsiang Low
2022	A	`conf`	Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning. AISTATS Yizhou Chen, Shizhuo Zhang, Bryan Kian Hsiang Low
2022	A	`conf`	Neural ensemble search via Bayesian sampling. UAI Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low
2022	J	`jnl`	On Provably Robust Meta-Bayesian Optimization. CoRR Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet
2022	A	`conf`	On provably robust meta-Bayesian optimization. UAI Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet
2022	A*	`conf`	On the Convergence of the Shapley Value in Parametric Bayesian Learning Games. ICML Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low
2022	J	`jnl`	On the Convergence of the Shapley Value in Parametric Bayesian Learning Games. CoRR Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low
2022	J	`jnl`	Rectified Max-Value Entropy Search for Bayesian Optimization. CoRR Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
2022	A*	`conf`	Sample-Then-Optimize Batch Neural Thompson Sampling. NeurIPS Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
2022	J	`jnl`	Sample-Then-Optimize Batch Neural Thompson Sampling. CoRR Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
2022	A*	`conf`	Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning. NeurIPS Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
2022	A*	`conf`	Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. NeurIPS Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low
2022	J	`jnl`	Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. CoRR Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low
2021	A*	`conf`	An Information-Theoretic Framework for Unifying Active Learning Problems. AAAI Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
2021	A*	`conf`	Collaborative Bayesian Optimization with Fair Regret. ICML Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet
2021	B	`conf`	Convolutional Normalizing Flows for Deep Gaussian Processes. IJCNN Haibin Yu, Dapeng Liu, Bryan Kian Hsiang Low, Patrick Jaillet
2021	J	`jnl`	Convolutional Normalizing Flows for Deep Gaussian Processes. CoRR Haibin Yu, Dapeng Liu, Bryan Kian Hsiang Low, Patrick Jaillet
2021	A*	`conf`	Differentially Private Federated Bayesian Optimization with Distributed Exploration. NeurIPS Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
2021	J	`jnl`	Differentially Private Federated Bayesian Optimization with Distributed Exploration. CoRR Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
2021	A*	`conf`	Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. NeurIPS Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low
2021	J	`jnl`	Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. CoRR Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low
2021	J	`jnl`	Going Beyond Neural Architecture Search with Sampling-based Neural Ensemble Search. CoRR Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low
2021	A*	`conf`	Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning. NeurIPS Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low
2021	J	`jnl`	Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. CoRR Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low
2021	A	`conf`	Learning to learn with Gaussian processes. UAI Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
2021	A*	`conf`	Model Fusion for Personalized Learning. ICML Thanh Chi Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet
2021	J	`jnl`	NASI: Label- and Data-agnostic Neural Architecture Search at Initialization. CoRR Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, Bryan Kian Hsiang Low
2021	A*	`conf`	Optimizing Conditional Value-At-Risk of Black-Box Functions. NeurIPS Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
2021	A*	`conf`	Top-k Ranking Bayesian Optimization. AAAI Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low, Patrick Jaillet
2021	J	`jnl`	Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization. CoRR Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet
2021	A	`conf`	Trusted-maximizers entropy search for efficient Bayesian optimization. UAI Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet
2021	A*	`conf`	Validation Free and Replication Robust Volume-based Data Valuation. NeurIPS Xinyi Xu, Zhaoxuan Wu, Chuan Sheng Foo, Bryan Kian Hsiang Low
2021	A*	`conf`	Value-at-Risk Optimization with Gaussian Processes. ICML Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
2021	J	`jnl`	Value-at-Risk Optimization with Gaussian Processes. CoRR Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
2020	J	`jnl`	An Information-Theoretic Framework for Unifying Active Learning Problems. CoRR Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
2020	A*	`conf`	Collaborative Machine Learning with Incentive-Aware Model Rewards. ICML Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low
2020	J	`jnl`	Collaborative Machine Learning with Incentive-Aware Model Rewards. CoRR Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low
2020	A*	`conf`	Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization. NeurIPS Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh
2020	J	`jnl`	Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization. CoRR Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh
2020	A	`conf`	FCM-sketch: generic network measurements with data plane support. CoNEXT Cha Hwan Song, Pravein Govindan Kannan, Bryan Kian Hsiang Low, Mun Choon Chan
2020	A*	`conf`	Federated Bayesian Optimization via Thompson Sampling. NeurIPS Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
2020	J	`jnl`	Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception. Auton. Robots Ruofei Ouyang, Bryan Kian Hsiang Low
2020	A*	`conf`	Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion. ICML Trong Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet
2020	A	`conf`	Nonmyopic Gaussian Process Optimization with Macro-Actions. AISTATS Dmitrii Kharkovskii, Chun Kai Ling, Bryan Kian Hsiang Low
2020	A*	`conf`	Private Outsourced Bayesian Optimization. ICML Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low
2020	J	`jnl`	Private Outsourced Bayesian Optimization. CoRR Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low
2020	A*	`conf`	R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games. ICML Zhongxiang Dai, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho
2020	A*	`conf`	Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression. AAAI Tong Teng, Jie Chen, Yehong Zhang, Bryan Kian Hsiang Low
2020	J	`jnl`	Top-k Ranking Bayesian Optimization. CoRR Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low, Patrick Jaillet
2020	A*	`conf`	Variational Bayesian Unlearning. NeurIPS Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
2020	J	`jnl`	Variational Bayesian Unlearning. CoRR Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
2019	A*	`conf`	Bayesian Optimization Meets Bayesian Optimal Stopping. ICML Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet
2019	A	`conf`	Bayesian Optimization with Binary Auxiliary Information. UAI Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low
2019	A*	`conf`	Collective Model Fusion for Multiple Black-Box Experts. ICML Quang Minh Hoang, Trong Nghia Hoang, Bryan Kian Hsiang Low, Carl Kingsford
2019	A*	`conf`	Implicit Posterior Variational Inference for Deep Gaussian Processes. NeurIPS Haibin Yu, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Zhongxiang Dai
2019	B	`conf`	Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression. IJCNN Haibin Yu, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet
2019	A*	`conf`	Towards Robust ResNet: A Small Step but a Giant Leap. IJCAI Jingfeng Zhang, Bo Han, Laura Wynter, Bryan Kian Hsiang Low, Mohan S. Kankanhalli
2017	A*	`conf`	Distributed Batch Gaussian Process Optimization. ICML Erik A. Daxberger, Bryan Kian Hsiang Low
2016	A*	`conf`	A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models. ICML Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low
2015	A*	`conf`	A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data. ICML Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low
2014	A*	`conf`	Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes. ICML Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli