Bryan Kian Hsiang Low

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 ε-Bayes-Optimal Active Learning of Gaussian Processes ICML 2014