Zhongxiang Dai
35 papers · 2019–2026 · 8 conferences · across top CS/AI conferences
Achievements
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πΊοΈ Taxonomy Completionist (11) π§ Keyword Pioneer π Interdisciplinary Bridge π Renaissance Researcher (5) π Conference Polyglot (7)
π
Renaissance Researcher
(5)
π
Interdisciplinary Bridge
πΊοΈ
Taxonomy Completionist
(11)
π€
Dynamic Duo
(30)
π
Triple Crown
π
Grand Slam
π¬
Deep Specialist
(12)
π₯
Unstoppable
(7)
β‘
Prolific Year
(5)
ποΈ
Keyword Collector
(95)
β
The Questioner
π
Century Club
(32)
π
Conference Pioneer
Conferences
NIPS (12)
ICML (9)
ICLR (5)
ACL (3)
UAI (3)
AAAI (1)
EMNLP (1)
JMLR (1)
Top co-authors
Keywords
bayesian optimization
(13)
gaussian process
(12)
thompson sampling
(7)
regret bound
(7)
large language model
(5)
upper confidence bound
(5)
black-box optimization
(4)
federated learning
(3)
in-context learning
(3)
generalization performance
(2)
hyperparameter optimization
(2)
acquisition function
(2)
prompt optimization
(2)
dueling bandit
(2)
neural network
(2)
neural tangent kernel
(2)
multi-armed bandit
(2)
neural architecture search
(2)
batch optimization
(2)
knowledge transfer
(1)
Papers
Large Language Model-Enhanced Multi-Armed Bandits
ACL 2026
Federated Linear Dueling Bandits
AAAI 2026
Self-Reflective Generation at Test Time
ACL 2026
Refining Adaptive Zeroth-Order Optimization at Ease
ICML 2025
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data
ACL 2025
Neural Dueling Bandits: Preference-Based Optimization with Human Feedback
ICLR 2025
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization
JMLR 2025
Online Clustering of Dueling Bandits
ICML 2025
Robustifying and Boosting Training-Free Neural Architecture Search
ICLR 2024
Localized Zeroth-Order Prompt Optimization
NIPS 2024
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars
NIPS 2024
Position Paper: Data-Centric AI in the Age of Large Language Models
EMNLP 2024
Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers
ICML 2024
Quantum Bayesian Optimization
NIPS 2023
Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation
ICLR 2023
Federated Neural Bandits
ICLR 2023
Exploiting Correlated Auxiliary Feedback in Parameterized Bandits
NIPS 2023
Training-Free Neural Active Learning with Initialization-Robustness Guarantees
ICML 2023
Batch Bayesian Optimization For Replicable Experimental Design
NIPS 2023
Neural ensemble search via Bayesian sampling
UAI 2022
Bayesian Optimization under Stochastic Delayed Feedback
ICML 2022
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search
NIPS 2022
Sample-Then-Optimize Batch Neural Thompson Sampling
NIPS 2022
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization
ICLR 2022
On provably robust meta-Bayesian optimization
UAI 2022
Value-at-Risk Optimization with Gaussian Processes
ICML 2021
Optimizing Conditional Value-At-Risk of Black-Box Functions
NIPS 2021
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
NIPS 2021
Differentially Private Federated Bayesian Optimization with Distributed Exploration
NIPS 2021
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
ICML 2020
Federated Bayesian Optimization via Thompson Sampling
NIPS 2020
Private Outsourced Bayesian Optimization
ICML 2020
Bayesian Optimization with Binary Auxiliary Information
UAI 2019
Implicit Posterior Variational Inference for Deep Gaussian Processes
NIPS 2019
Bayesian Optimization Meets Bayesian Optimal Stopping
ICML 2019