77 citations · 584 across the 84 of their papers we have counts for
9 papers · 2 filters
Probably Approximate Shapley Fairness with Applications in Machine Learning
Zijian Zhou, Xinyi Xu, Rachael Hwee Ling Sim +2
The Shapley value (SV) is adopted in various scenarios in machine learning (ML), including data valuation, agent valuation, and feature attribution, as it satisfies their fairness…
Bayesian Optimization under Stochastic Delayed Feedback
Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low
Bayesian optimization (BO) is a widely-used sequential method for zeroth-order optimization of complex and expensive-to-compute black-box functions. The existing BO methods assume…
On Provably Robust Meta-Bayesian Optimization
Zhongxiang Dai, Yizhou Chen, Haibin Yu +2
Bayesian optimization (BO) has become popular for sequential optimization of black-box functions. When BO is used to optimize a target function, we often have access to previous ev…
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games
Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low
Measuring contributions is a classical problem in cooperative game theory where the Shapley value is the most well-known solution concept. In this paper, we establish the convergen…
Federated Neural Bandits
Zhongxiang Dai, Yao Shu, Arun Verma +3
Recent works on neural contextual bandits have achieved compelling performances due to their ability to leverage the strong representation power of neural networks (NNs) for reward…
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization
Shouri Hu, Haowei Wang, Zhongxiang Dai +2
The expected improvement (EI) is one of the most popular acquisition functions for Bayesian optimization (BO) and has demonstrated good empirical performances in many applications…
Rectified Max-Value Entropy Search for Bayesian Optimization
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Although the existing max-value entropy search (MES) is based on the widely celebrated notion of mutual information, its empirical performance can suffer due to two misconceptions…
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten
Quoc Phong Nguyen, Ryutaro Oikawa, Dinil Mon Divakaran +2
As the use of machine learning (ML) models is becoming increasingly popular in many real-world applications, there are practical challenges that need to be addressed for model main…
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search
Yao Shu, Zhongxiang Dai, Zhaoxuan Wu +1
Neural architecture search (NAS) has gained immense popularity owing to its ability to automate neural architecture design. A number of training-free metrics are recently proposed…