Search Results for author:
Found 22 papers, 9 papers with code
Probabilistic Model-Based Dynamic Architecture Search
The experimental results show that the proposed architecture search method is fast and can achieve comparable performance to the existing methods.
Covariance Matrix Adaptation Evolution Strategy for Low Effective Dimensionality
Despite the state-of-the-art performance of the covariance matrix adaptation evolution strategy (CMA-ES), high-dimensional black-box optimization problems are challenging tasks.
CMA-ES for Discrete and Mixed-Variable Optimization on Sets of Points
Discrete and mixed-variable optimization problems have appeared in several real-world applications.
Tail Bounds on the Runtime of Categorical Compact Genetic Algorithm
We term this specific algorithm the categorical compact genetic algorithm (ccGA).
Natural Gradient Interpretation of Rank-One Update in CMA-ES
While the rank-one update makes the covariance matrix to increase the likelihood of generating a solution in the direction of the evolution path, this idea has been difficult to formulate and interpret as a natural gradient method unlike the rank-$\mu$ update.
CMA-ES with Adaptive Reevaluation for Multiplicative Noise
We derive that the set of maximizers of the noise-independent utility, which is used in the reevaluation technique, certainly contains the optimal solution, while the noise-dependent utility, which is used in the population size and leaning rate adaptations, does not satisfy it under multiplicative noise.
CMA-ES for Safe Optimization
This optimization setting is known as safe optimization and formulated as a specialized type of constrained optimization problem with constraints for safety functions.
CatCMA : Stochastic Optimization for Mixed-Category Problems
CatCMA updates the parameters of the joint probability distribution in the natural gradient direction.
(1+1)-CMA-ES with Margin for Discrete and Mixed-Integer Problems
The margin correction has been applied to ($\mu/\mu_\mathrm{w}$,$\lambda$)-CMA-ES, while this paper introduces the margin correction into (1+1)-CMA-ES, an elitist version of CMA-ES.
Simple Domain Generalization Methods are Strong Baselines for Open Domain Generalization
In real-world applications, a machine learning model is required to handle an open-set recognition (OSR), where unknown classes appear during the inference, in addition to a domain shift, where the distribution of data differs between the training and inference phases.
Marginal Probability-Based Integer Handling for CMA-ES Tackling Single-and Multi-Objective Mixed-Integer Black-Box Optimization
If the CMA-ES is applied to the MI-BBO with straightforward discretization, however, the variance corresponding to the integer variables becomes much smaller than the granularity of the discretization before reaching the optimal solution, which leads to the stagnation of the optimization.
Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computing
This paper proposes a neural architecture search (NAS) method for split computing.
Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling
The proposed method allows us to obtain multiple architectures with different complexities in a single architecture search, resulting in reducing the search cost.
CMA-ES with Margin: Lower-Bounding Marginal Probability for Mixed-Integer Black-Box Optimization
If the CMA-ES is applied to the MI-BBO with straightforward discretization, however, the variance corresponding to the integer variables becomes much smaller than the granularity of the discretization before reaching the optimal solution, which leads to the stagnation of the optimization.
A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization
The first phase in TPB aims to approximate a few Pareto optimal solutions by optimizing a sequence of single-objective scalar problems.
NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters
Building the benchmark dataset for joint optimization of architecture and training hyperparameters is essential to further NAS research.
Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures
We focus on the probabilistic model-based dynamic neural network structure optimization that considers the probability distribution of structure parameters and simultaneously optimizes both the distribution parameters and connection weights based on gradient methods.
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
It accepts arbitrary search space (widely-applicable) and enables to employ a gradient-based simultaneous optimization of weights and architecture (fast).
Parameterless Stochastic Natural Gradient Method for Discrete Optimization and its Application to Hyper-Parameter Optimization for Neural Network
This paper proposes a parameterless BBDO algorithm based on information geometric optimization, a recent framework for black box optimization using stochastic natural gradient.
Sample Reuse via Importance Sampling in Information Geometric Optimization
The experimental results show that the sample reuse helps to reduce the number of function evaluations on many benchmark functions for both the PBIL and the pure rank-$\mu$ update CMA-ES.
Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling
We consider a probability distribution that generates network structures, and optimize the parameters of the distribution instead of directly optimizing the network structure.
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
To evaluate the proposed method, we constructed a CNN architecture for the image classification task with the CIFAR-10 dataset.
