Bryan Kian Hsiang Low
Associate Professor
Works on probabilistic and data-efficient machine learning, including Bayesian optimisation, Gaussian processes and federated and collaborative learning.
Bryan Kian Hsiang Low's lab at NUS works on probabilistic & causal inference, trustworthy & safe ML and efficient training & inference. 89 papers since 2023, 79 at top venues, mostly ICLR and ICML. 28 PhD students. 40 alumni, most often to Meta and Amazon. Applications close .
At a glance
- papers, 2023–2026
- 23/ year
- at T1 venues
- 89%
- PhD students
- 28
- MS students
- 0
- alumni listed
- 40
Papers by yearfrom the lab homepage, 2023–2026
- 28 papers2026 · T1 21 · collecting
- 17 papers2025 · T1 16
- 26 papers2024 · T1 25
- 18 papers2023 · T1 17
T1 venuesOtherVenues: ICLR, ICML, NeurIPS, EMNLP, AAAI
Research focuspaper titles that use each topic's words
- Efficient training & inference17 papers
- Probabilistic & causal inference12 papers
- Trustworthy & safe ML3 papers
- collaborative learning1 paper
- bayesian optimisation0 papers
Paper list
From the lab homepage.
202628 papers
- ICLR (T1)ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment
- ICML (T1)Adalina: Adaptive Linear Approximation for the Shapley Value and Beyond
- ICML WorkshopBalance Human Agency and AI Assistance in the Tussle for the "Right" to Choose, Own, Work, and Learn
- ICML WorkshopBoLT: A Benchmark to Democratize Black-box Optimization Research for Expensive LLM Tasks
- COLMCORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery
- ICML (T1)De-attribute to Forget for LLM Unlearning
- AISTATS (T1)Differentially Private Algorithms for the Stochastic Compositional Optimization Problem
- ICLR (T1)DUET: Optimizing LLM Training Data Mixtures via Noisy Feedback from Unseen, Downstream Evaluation Tasks
- ACL (T1)EULoInf: Efficient Hessian-Free Entropy Based Uncertainty-Aware Data Influence Approximation
- ICML (T1)How Hard Can It Be? Hardness-Aware Multi-Objective Unlearning
- ICLR (T1)How to Cure Newton for Unlearning Neural Networks? An Empirical Study from the Hessian Perspective
- AAMAS (T1)Incentivizing Black-Box Model Sharing with Fair Rewards and Payoffs
202517 papers
- JMLR (T1)Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization
- ICML (T1)BILBO: BILevel Bayesian Optimization
- ICLR (T1)Broaden your SCOPE! Efficient Multi-Turn Conversation Planning for LLMs with Semantic Space
- EMNLP (T1)Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks
- AAMAS (T1)DUPRE: Data Utility Prediction for Efficient Data Valuation
- ICLR (T1)Efficient Top-m Data Values Identification for Data Selection
- ICML (T1)Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
- ICLR (T1)Group-robust Sample Reweighting for Subpopulation Shifts via Influence Functions
- NeurIPS (T1)Incentivizing Time-Aware Fairness in Data Sharing
- ICLR (T1)Neural Dueling Bandits: Preference-Based Optimization with Human Feedback
- ICML (T1)NICE: Data Selection for Instruction Tuning in LLMs with Non-differentiable Evaluation Metric
- AAAI (T1)Paid with Models: Optimal Contract Design for Collaborative Machine Learning
202426 papers
- ICLR (T1)A Unified Framework for Bayesian Optimization under Contextual Uncertainty
- NeurIPS (T1)Active Set Ordering
- NeurIPS (T1)Data Distribution Valuation
- AAAI (T1)Decentralized Sum-of-Nonconvex Optimization
- ICML (T1)Deletion-Anticipative Data Selection with a Limited Budget
- TMLR (T1)Dependency Structure Search Bayesian Optimization for Decision Making Models
- AAAI (T1)DeRDaVa: Deletion-Robust Data Valuation for Machine Learning
- NeurIPS (T1)DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning
- ICML (T1)Distributionally Robust Data Valuation
- NeurIPS (T1)Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization
- ICML (T1)Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions
- ICML WorkshopHeterogeneous Federated Zeroth-Order Optimization using Gradient Surrogates
202318 papers
- NeurIPS (T1)Batch Bayesian Optimization for Replicable Experimental Design
- NeurIPS (T1)Bayesian Optimization with Cost-varying Variable Subsets
- ICML (T1)Collaborative Causal Inference with Fair Incentives
- NeurIPS (T1)Exploiting Correlated Auxiliary Feedback in Parameterized Bandits
- ICML (T1)Fair yet Asymptotically Equal Collaborative Learning
- AISTATS (T1)FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery
- ICLR (T1)Federated Neural Bandits
- AAMAS (T1)FedHQL: Federated Heterogeneous Q-Learning
- NeurIPS (T1)Incentives in Private Collaborative Machine Learning
- NeurIPS (T1)Model Shapley: Equitable Model Valuation with Black-box Access
- AISTATS (T1)No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities
- AAAI (T1)Probably Approximate Shapley Fairness with Applications in Machine Learning
Where alumni went40 listed, 63% to industry
Industry 20Academia 12
Most often to
- Meta
- Amazon
- ByteDance
Also to
- NUS
- Carnegie Mellon University
- College of Computer Science and Software Engineering
Apply to NUSFall 2027
School of Computing PhD in Computer Science
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