Zhongxiang Dai | DeepAI

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Featured Co-authors

Roger Wattenhofer
119 publications
Patrick Jaillet
54 publications
Beng Chin Ooi
44 publications
Kian Hsiang Low
28 publications
Bryan Kian Hsiang Low
27 publications
Cheston Tan
25 publications
See-kiong Ng
24 publications
Wei Jing
16 publications
Yizhou Chen
16 publications
Arun Verma
15 publications
Yao Shu
11 publications

∙ 08/08/2023

Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients

Federated optimization, an emerging paradigm which finds wide real-world...

0 Yao Shu, et al. ∙

∙ 06/07/2023

Training-Free Neural Active Learning with Initialization-Robustness Guarantees

Existing neural active learning algorithms have aimed to optimize the pr...

0 Apivich Hemachandra, et al. ∙

∙ 01/26/2023

FedHQL: Federated Heterogeneous Q-Learning

Federated Reinforcement Learning (FedRL) encourages distributed agents t...

0 Flint Xiaofeng Fan, et al. ∙

∙ 10/13/2022

Sample-Then-Optimize Batch Neural Thompson Sampling

Bayesian optimization (BO), which uses a Gaussian process (GP) as a surr...

0 Zhongxiang Dai, et al. ∙

∙ 06/19/2022

Bayesian Optimization under Stochastic Delayed Feedback

Bayesian optimization (BO) is a widely-used sequential method for zeroth...

0 Arun Verma, et al. ∙

∙ 06/14/2022

On Provably Robust Meta-Bayesian Optimization

Bayesian optimization (BO) has become popular for sequential optimizatio...

0 Zhongxiang Dai, et al. ∙

∙ 05/28/2022

Federated Neural Bandit

Recent works on neural contextual bandit have achieved compelling perfor...

0 Zhongxiang Dai, et al. ∙

∙ 05/10/2022

Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization

The expected improvement (EI) is one of the most popular acquisition fun...

0 Shouri Hu, et al. ∙

∙ 01/24/2022

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

Neural architecture search (NAS) has gained immense popularity owing to ...

0 Yao Shu, et al. ∙

∙ 10/27/2021

Differentially Private Federated Bayesian Optimization with Distributed Exploration

Bayesian optimization (BO) has recently been extended to the federated l...

14 Zhongxiang Dai, et al. ∙

∙ 10/26/2021

Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee

The growing literature of Federated Learning (FL) has recently inspired ...

0 Flint Xiaofeng Fan, et al. ∙

∙ 09/06/2021

Going Beyond Neural Architecture Search with Sampling-based Neural Ensemble Search

Recently, Neural Architecture Search (NAS) has been widely applied to au...

0 Yao Shu, et al. ∙

∙ 09/02/2021

NASI: Label- and Data-agnostic Neural Architecture Search at Initialization

Recent years have witnessed a surging interest in Neural Architecture Se...

0 Yao Shu, et al. ∙

∙ 05/13/2021

Value-at-Risk Optimization with Gaussian Processes

Value-at-risk (VaR) is an established measure to assess risks in critica...

29 Quoc Phong Nguyen, et al. ∙

∙ 10/24/2020

Private Outsourced Bayesian Optimization

This paper presents the private-outsourced-Gaussian process-upper confid...

0 Dmitrii Kharkovskii, et al. ∙

∙ 10/20/2020

Federated Bayesian Optimization via Thompson Sampling

Bayesian optimization (BO) is a prominent approach to optimizing expensi...

17 Zhongxiang Dai, et al. ∙

∙ 06/30/2020

R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games

This paper presents a recursive reasoning formalism of Bayesian optimiza...

11 Zhongxiang Dai, et al. ∙

∙ 10/26/2019

Implicit Posterior Variational Inference for Deep Gaussian Processes

A multi-layer deep Gaussian process (DGP) model is a hierarchical compos...

16 Haibin Yu, et al. ∙

∙ 06/17/2019

Bayesian Optimization with Binary Auxiliary Information

This paper presents novel mixed-type Bayesian optimization (BO) algorith...

7 Yehong Zhang, et al. ∙

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