Bryan Kian Hsiang Low

papers

613

total citations

papers (27)

Differentially Private Federated Bayesian Optimization with Distributed Exploration

NEURIPS 2021arXiv

citations

Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search

NEURIPS 2022arXiv

citations

Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization

NEURIPS 2020arXiv

citations

Sample-Then-Optimize Batch Neural Thompson Sampling

NEURIPS 2022arXiv

citations

Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers

ICML 2024arXiv

citations

Quantum Bayesian Optimization

NEURIPS 2023arXiv

citations

Incentives in Private Collaborative Machine Learning

NEURIPS 2023arXiv

citations

Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions

ICML 2024arXiv

citations

Batch Bayesian Optimization For Replicable Experimental Design

NEURIPS 2023arXiv

citations

Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates

AAAI 2024arXiv

citations

DeRDaVa: Deletion-Robust Data Valuation for Machine Learning

AAAI 2024arXiv

citations

Exploiting Correlated Auxiliary Feedback in Parameterized Bandits

NEURIPS 2023arXiv

citations

Distributionally Robust Data Valuation

ICML 2024

citations

Deletion-Anticipative Data Selection with a Limited Budget

ICML 2024

citations

Optimizing Conditional Value-At-Risk of Black-Box Functions

NEURIPS 2021

citations

Bayesian Optimization with Cost-varying Variable Subsets

NEURIPS 2023

citations

Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning

NEURIPS 2022

citations

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

NEURIPS 2021

citations

Validation Free and Replication Robust Volume-based Data Valuation

NEURIPS 2021

citations

Model Shapley: Equitable Model Valuation with Black-box Access

NEURIPS 2023

citations

Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components

ICML 2024

citations

Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization

ICML 2024

citations

Decentralized Sum-of-Nonconvex Optimization

AAAI 2024arXiv

citations

Paid with Models: Optimal Contract Design for Collaborative Machine Learning

AAAI 2025arXiv

citations

Bryan Kian Hsiang Low

papers (27)

Variational Bayesian Unlearning

Federated Bayesian Optimization via Thompson Sampling

Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee

Differentially Private Federated Bayesian Optimization with Distributed Exploration

Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search

Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization

Sample-Then-Optimize Batch Neural Thompson Sampling

Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers

Quantum Bayesian Optimization

Incentives in Private Collaborative Machine Learning

Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions

Batch Bayesian Optimization For Replicable Experimental Design

Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates

DeRDaVa: Deletion-Robust Data Valuation for Machine Learning

Exploiting Correlated Auxiliary Feedback in Parameterized Bandits

Distributionally Robust Data Valuation

Deletion-Anticipative Data Selection with a Limited Budget

Optimizing Conditional Value-At-Risk of Black-Box Functions

Bayesian Optimization with Cost-varying Variable Subsets

Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

Validation Free and Replication Robust Volume-based Data Valuation

Model Shapley: Equitable Model Valuation with Black-box Access

Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components

Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization

Decentralized Sum-of-Nonconvex Optimization

Paid with Models: Optimal Contract Design for Collaborative Machine Learning

papers (27)

Variational Bayesian Unlearning

Federated Bayesian Optimization via Thompson Sampling

Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee

Differentially Private Federated Bayesian Optimization with Distributed Exploration

Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search

Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization

Sample-Then-Optimize Batch Neural Thompson Sampling

Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers

Quantum Bayesian Optimization

Incentives in Private Collaborative Machine Learning

Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions

Batch Bayesian Optimization For Replicable Experimental Design

Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates

DeRDaVa: Deletion-Robust Data Valuation for Machine Learning

Exploiting Correlated Auxiliary Feedback in Parameterized Bandits

Distributionally Robust Data Valuation

Deletion-Anticipative Data Selection with a Limited Budget

Optimizing Conditional Value-At-Risk of Black-Box Functions

Bayesian Optimization with Cost-varying Variable Subsets

Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

Validation Free and Replication Robust Volume-based Data Valuation

Model Shapley: Equitable Model Valuation with Black-box Access

Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components

Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization

Decentralized Sum-of-Nonconvex Optimization

Paid with Models: Optimal Contract Design for Collaborative Machine Learning