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08/08/2023
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients
Federated optimization, an emerging paradigm which finds wide real-world...
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06/07/2023
Training-Free Neural Active Learning with Initialization-Robustness Guarantees
Existing neural active learning algorithms have aimed to optimize the pr...
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01/26/2023
FedHQL: Federated Heterogeneous Q-Learning
Federated Reinforcement Learning (FedRL) encourages distributed agents t...
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10/13/2022
Sample-Then-Optimize Batch Neural Thompson Sampling
Bayesian optimization (BO), which uses a Gaussian process (GP) as a surr...
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06/19/2022
Bayesian Optimization under Stochastic Delayed Feedback
Bayesian optimization (BO) is a widely-used sequential method for zeroth...
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06/14/2022
On Provably Robust Meta-Bayesian Optimization
Bayesian optimization (BO) has become popular for sequential optimizatio...
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05/28/2022
Federated Neural Bandit
Recent works on neural contextual bandit have achieved compelling perfor...
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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...
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01/24/2022
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search
Neural architecture search (NAS) has gained immense popularity owing to ...
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10/27/2021
Differentially Private Federated Bayesian Optimization with Distributed Exploration
Bayesian optimization (BO) has recently been extended to the federated l...
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10/26/2021
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
The growing literature of Federated Learning (FL) has recently inspired ...
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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...
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09/02/2021
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization
Recent years have witnessed a surging interest in Neural Architecture Se...
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05/13/2021
Value-at-Risk Optimization with Gaussian Processes
Value-at-risk (VaR) is an established measure to assess risks in critica...
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10/24/2020
Private Outsourced Bayesian Optimization
This paper presents the private-outsourced-Gaussian process-upper confid...
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10/20/2020
Federated Bayesian Optimization via Thompson Sampling
Bayesian optimization (BO) is a prominent approach to optimizing expensi...
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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...
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10/26/2019
Implicit Posterior Variational Inference for Deep Gaussian Processes
A multi-layer deep Gaussian process (DGP) model is a hierarchical compos...
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06/17/2019
