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Found 26 papers, 15 papers with code
Harnessing the Latent Diffusion Model for Training-Free Image Style Transfer
Through a series of experiments and a user study, we show that our method can quickly transfer the style of an image without additional training.
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.
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
We explore off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits (CCB), where a policy selects a subset in the action space.
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 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.
Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It
There has been a growing interest in off-policy evaluation in the literature such as recommender systems and personalized medicine.
Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction
The PseudoInverse (PI) estimator has been introduced to mitigate the variance issue by assuming linearity in the reward function, but this can result in significant bias as this assumption is hard-to-verify from observed data and is often substantially violated.
cmaes : A Simple yet Practical Python Library for CMA-ES
To address the need for an accessible yet potent tool in this domain, we developed cmaes, a simple and practical Python library for CMA-ES.
CMA-ES with Learning Rate Adaptation
The results show that the CMA-ES with the proposed learning rate adaptation works well for multimodal and/or noisy problems without extremely expensive learning rate tuning.
(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.
CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems?
The results demonstrate that, when the proposed learning rate adaptation is used, the CMA-ES with default population size works well on multimodal and/or noisy problems, without the need for extremely expensive learning rate tuning.
Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes
This observation motivates us to improve the MCMC-based estimation for skew GP, for which we show the practical efficiency of Gibbs sampling and derive the low variance MC estimator.
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.
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.
Fast Moving Natural Evolution Strategy for High-Dimensional Problems
In this work, we propose a new variant of natural evolution strategies (NES) for high-dimensional black-box optimization problems.
Optimal Best Arm Identification in Two-Armed Bandits with a Fixed Budget under a Small Gap
We show that a strategy following the Neyman allocation rule (Neyman, 1934) is asymptotically optimal when the gap between the expected rewards is small.
Towards a Principled Learning Rate Adaptation for Natural Evolution Strategies
On the other hand, in problems that are difficult to optimize (e. g., multimodal functions), the proposed mechanism makes it possible to set a conservative learning rate when the estimation accuracy of the natural gradient seems to be low, which results in the robust and stable search.
Takeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime
Generalization measures are intensively studied in the machine learning community for better modeling generalization gaps.
Natural Evolution Strategy for Unconstrained and Implicitly Constrained Problems with Ridge Structure
However, DX-NES-IC has a problem in that the moving speed of the probability distribution is slow on ridge structure.
Warm Starting CMA-ES for Hyperparameter Optimization
Hyperparameter optimization (HPO), formulated as black-box optimization (BBO), is recognized as essential for automation and high performance of machine learning approaches.
Multi-Source Unsupervised Hyperparameter Optimization
How can we conduct efficient hyperparameter optimization for a completely new task?
Simple and Scalable Parallelized Bayesian Optimization
In recent years, leveraging parallel and distributed computational resources has become essential to solve problems of high computational cost.
Efficient Hyperparameter Optimization under Multi-Source Covariate Shift
This assumption is, however, often violated in uncertain real-world applications, which motivates the study of learning under covariate shift.
A Simple Heuristic for Bayesian Optimization with A Low Budget
The aim of black-box optimization is to optimize an objective function within the constraints of a given evaluation budget.
Towards Resolving Propensity Contradiction in Offline Recommender Learning
We study offline recommender learning from explicit rating feedback in the presence of selection bias.
