77 citations · 584 across the 84 of their papers we have counts for
8 papers · 1 filter
Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards
Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo +1
This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data to a pool for trainin…
Differentially Private Federated Bayesian Optimization with Distributed Exploration
Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Bayesian optimization (BO) has recently been extended to the federated learning (FL) setting by the federated Thompson sampling (FTS) algorithm, which has promising applications su…
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai +3
The growing literature of Federated Learning (FL) has recently inspired Federated Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better decision-…
Neural Ensemble Search via Bayesian Sampling
Yao Shu, Yizhou Chen, Zhongxiang Dai +1
Recently, neural architecture search (NAS) has been applied to automate the design of neural networks in real-world applications. A large number of algorithms have been developed t…
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization
Yao Shu, Shaofeng Cai, Zhongxiang Dai +2
Recent years have witnessed a surging interest in Neural Architecture Search (NAS). Various algorithms have been proposed to improve the search efficiency and effectiveness of NAS,…
Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization
Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low +1
Information-based Bayesian optimization (BO) algorithms have achieved state-of-the-art performance in optimizing a black-box objective function. However, they usually require sever…