← Authors

Bryan Kian Hsiang Low

98 papers · 2014–2026 · 11 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🌍 Conference Polyglot (10) πŸƒ Academic Marathon (11) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (10) 🌈 Renaissance Researcher (6) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (21) 🏠 Conference Loyalist (25) πŸ† Keyword Champion (2) πŸ‘‘ Triple Crown 🀝 Dynamic Duo (33) πŸ”¬ Deep Specialist (26) πŸ† Grand Slam πŸ“ˆ Trend Setter ❓ The Questioner (2) ⚑ Prolific Year (9) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (65) πŸ’Ž Century Club (97) πŸ”₯ Unstoppable (7)

Conferences

ICML (29) NIPS (25) ICLR (16) AAAI (9) UAI (5) AISTATS (4) EMNLP (4) ACL (2) IJCAI (2) EACL (1) JMLR (1)

Top co-authors

Patrick Jaillet (33) Zhongxiang Dai (30) Xinyi Xu (17) Quoc Phong Nguyen (16) YAO SHU (15) Zhaoxuan Wu (15) See-Kiong Ng (13) Xiaoqiang Lin (12) Arun Verma (11) Chuan Sheng Foo (10)

Research topics

Privacy (1)

Keywords

bayesian optimization (25) gaussian process (19) regret bound (10) data valuation (9) upper confidence bound (7) large language model (7) variational inference (6) shapley value (6) thompson sampling (6) federated learning (6) acquisition function (6) collaborative learning (5) collaborative machine learning (5) batch optimization (4) active learning (4) black-box optimization (4) in-context learning (3) entropy search (3) stochastic optimization (3) sample efficiency (3)

Papers

Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graph for Retrieval-Augmented Generation EACL 2026
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data ACL 2025
TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding ACL 2025
Paid with Models: Optimal Contract Design for Collaborative Machine Learning AAAI 2025
Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs with Semantic Space ICLR 2025
PIED: Physics-Informed Experimental Design for Inverse Problems ICLR 2025
Neural Dueling Bandits: Preference-Based Optimization with Human Feedback ICLR 2025
Group-robust Sample Reweighting for Subpopulation Shifts via Influence Functions ICLR 2025
Efficient Top-m Data Values Identification for Data Selection ICLR 2025
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization JMLR 2025
NICE Data Selection for Instruction Tuning in LLMs with Non-differentiable Evaluation Metric ICML 2025
Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models ICML 2025
BILBO: BILevel Bayesian Optimization ICML 2025
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning Tasks EMNLP 2025
Uncovering Scaling Laws for Large Language Models via Inverse Problems EMNLP 2025
Position Paper: Data-Centric AI in the Age of Large Language Models EMNLP 2024
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions ICML 2024
Deletion-Anticipative Data Selection with a Limited Budget ICML 2024
Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization ICML 2024
Distributionally Robust Data Valuation ICML 2024
Data Distribution Valuation NIPS 2024
DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning NIPS 2024
Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization NIPS 2024
Localized Zeroth-Order Prompt Optimization NIPS 2024
Active Set Ordering NIPS 2024
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars NIPS 2024
Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers ICML 2024
Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components ICML 2024
Decentralized Sum-of-Nonconvex Optimization AAAI 2024
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates AAAI 2024
DeRDaVa: Deletion-Robust Data Valuation for Machine Learning AAAI 2024
Waterfall: Scalable Framework for Robust Text Watermarking and Provenance for LLMs EMNLP 2024
A Unified Framework for Bayesian Optimization under Contextual Uncertainty ICLR 2024
PINNACLE: PINN Adaptive ColLocation and Experimental points selection ICLR 2024
Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes ICLR 2024
Optimistic Bayesian Optimization with Unknown Constraints ICLR 2024
Understanding Domain Generalization: A Noise Robustness Perspective ICLR 2024
Robustifying and Boosting Training-Free Neural Architecture Search ICLR 2024
Incentive-Aware Federated Learning with Training-Time Model Rewards ICLR 2024
FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery AISTATS 2023
Federated Neural Bandits ICLR 2023
Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation ICLR 2023
Incentives in Private Collaborative Machine Learning NIPS 2023
Collaborative Causal Inference with Fair Incentives ICML 2023
Probably Approximate Shapley Fairness with Applications in Machine Learning AAAI 2023
Exploiting Correlated Auxiliary Feedback in Parameterized Bandits NIPS 2023
Bayesian Optimization with Cost-varying Variable Subsets NIPS 2023
Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation ICLR 2023
Model Shapley: Equitable Model Valuation with Black-box Access NIPS 2023
Batch Bayesian Optimization For Replicable Experimental Design NIPS 2023
Quantum Bayesian Optimization NIPS 2023
Fair yet Asymptotically Equal Collaborative Learning ICML 2023
Training-Free Neural Active Learning with Initialization-Robustness Guarantees ICML 2023
No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities AISTATS 2023
Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning AISTATS 2022
Sample-Then-Optimize Batch Neural Thompson Sampling NIPS 2022
Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning NIPS 2022
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search NIPS 2022
Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards AAAI 2022
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization ICLR 2022
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games ICML 2022
Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity ICML 2022
Bayesian Optimization under Stochastic Delayed Feedback ICML 2022
DAVINZ: Data Valuation using Deep Neural Networks at Initialization ICML 2022
Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges IJCAI 2022
On provably robust meta-Bayesian optimization UAI 2022
Neural ensemble search via Bayesian sampling UAI 2022
Top-k Ranking Bayesian Optimization AAAI 2021
An Information-Theoretic Framework for Unifying Active Learning Problems AAAI 2021
Collaborative Bayesian Optimization with Fair Regret ICML 2021
Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning NIPS 2021
Validation Free and Replication Robust Volume-based Data Valuation NIPS 2021
Differentially Private Federated Bayesian Optimization with Distributed Exploration NIPS 2021
Model Fusion for Personalized Learning ICML 2021
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee NIPS 2021
Learning to learn with Gaussian processes UAI 2021
Trusted-maximizers entropy search for efficient Bayesian optimization UAI 2021
Optimizing Conditional Value-At-Risk of Black-Box Functions NIPS 2021
Value-at-Risk Optimization with Gaussian Processes ICML 2021
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games ICML 2020
Private Outsourced Bayesian Optimization ICML 2020
Collaborative Machine Learning with Incentive-Aware Model Rewards ICML 2020
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization NIPS 2020
Nonmyopic Gaussian Process Optimization with Macro-Actions AISTATS 2020
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression AAAI 2020
Variational Bayesian Unlearning NIPS 2020
Federated Bayesian Optimization via Thompson Sampling NIPS 2020
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion ICML 2020
Collective Model Fusion for Multiple Black-Box Experts ICML 2019
Towards Robust ResNet: A Small Step but a Giant Leap IJCAI 2019
Implicit Posterior Variational Inference for Deep Gaussian Processes NIPS 2019
Bayesian Optimization with Binary Auxiliary Information UAI 2019
Bayesian Optimization Meets Bayesian Optimal Stopping ICML 2019
Distributed Batch Gaussian Process Optimization ICML 2017
A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models ICML 2016
Inverse Reinforcement Learning with Locally Consistent Reward Functions NIPS 2015
A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data ICML 2015
Nonmyopic Ξ΅-Bayes-Optimal Active Learning of Gaussian Processes ICML 2014