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05/01/2023
(1+1)-CMA-ES with Margin for Discrete and Mixed-Integer Problems
The covariance matrix adaptation evolution strategy (CMA-ES) is an effic...
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03/31/2023
Simple Domain Generalization Methods are Strong Baselines for Open Domain Generalization
In real-world applications, a machine learning model is required to hand...
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12/19/2022
CMA-ES with Margin for Single-and Multi-Objective Mixed-Integer Black-Box Optimization
This study targets the mixed-integer black-box optimization (MI-BBO) pro...
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08/30/2022
Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computing
This paper proposes a neural architecture search (NAS) method for split ...
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07/21/2022
Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling
Neural architecture search (NAS) aims to automate architecture design pr...
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05/26/2022
CMA-ES with Margin: Lower-Bounding Marginal Probability for Mixed-Integer Black-Box Optimization
This study targets the mixed-integer black-box optimization (MI-BBO) pro...
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03/29/2022
A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization
This paper proposes a two-phase framework with a Bézier simplex-based in...
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10/19/2021
NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters
The benchmark datasets for neural architecture search (NAS) have been de...
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07/15/2019
Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures
A method of simultaneously optimizing both the structure of neural netwo...
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05/21/2019
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
High sensitivity of neural architecture search (NAS) methods against the...
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09/18/2018
Parameterless Stochastic Natural Gradient Method for Discrete Optimization and its Application to Hyper-Parameter Optimization for Neural Network
Black box discrete optimization (BBDO) appears in wide range of engineer...
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05/31/2018
Sample Reuse via Importance Sampling in Information Geometric Optimization
In this paper we propose a technique to reduce the number of function ev...
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01/23/2018
Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling
Deep neural networks (DNNs) are powerful machine learning models and hav...
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04/03/2017