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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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08/01/2023
Hessian-Aware Bayesian Optimization for Decision Making Systems
Many approaches for optimizing decision making systems rely on gradient ...
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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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05/23/2023
Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks
We introduce Goat, a fine-tuned LLaMA model that significantly outperfor...
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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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02/28/2022
Rectified Max-Value Entropy Search for Bayesian Optimization
Although the existing max-value entropy search (MES) is based on the wid...
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02/28/2022
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten
As the use of machine learning (ML) models is becoming increasingly popu...
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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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07/30/2021
Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization
Information-based Bayesian optimization (BO) algorithms have achieved st...
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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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04/17/2021
Convolutional Normalizing Flows for Deep Gaussian Processes
Deep Gaussian processes (DGPs), a hierarchical composition of GP models,...
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12/19/2020
An Information-Theoretic Framework for Unifying Active Learning Problems
This paper presents an information-theoretic framework for unifying acti...
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12/19/2020
Top-k Ranking Bayesian Optimization
This paper presents a novel approach to top-k ranking Bayesian optimizat...
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11/17/2020
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
The problem of inverse reinforcement learning (IRL) is relevant to a var...
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10/24/2020
Variational Bayesian Unlearning
This paper studies the problem of approximately unlearning a Bayesian mo...
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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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10/24/2020
Collaborative Machine Learning with Incentive-Aware Model Rewards
Collaborative machine learning (ML) is an appealing paradigm to build hi...
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10/23/2019
