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Found 16 papers, 6 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.
Bandits with Abstention under Expert Advice
We study the classic problem of prediction with expert advice under bandit feedback.
Multi-class Graph Clustering via Approximated Effective $p$-Resistance
We prove upper and lower bounds on this approximation and observe that it is exact when the graph is a tree.
Prediction Algorithms Achieving Bayesian Decision Theoretical Optimality Based on Decision Trees as Data Observation Processes
In the field of decision trees, most previous studies have difficulty ensuring the statistical optimality of a prediction of new data and suffer from overfitting because trees are usually used only to represent prediction functions to be constructed from given data.
(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.
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.
Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel $k$-means, and Heat Kernel
For graph cut based spectral clustering, it is common to model real-valued data into graph by modeling pairwise similarities using kernel function.
Probability Distribution on Rooted Trees
The hierarchical and recursive expressive capability of rooted trees is applicable to represent statistical models in various areas, such as data compression, image processing, and machine learning.
Probability Distribution on Full Rooted Trees
Its parametric representation is suitable for calculating the properties of our distribution using recursive functions, such as the mode, expectation, and posterior distribution.
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.
Hypergraph $p$-Laplacian: A Differential Geometry View
The proposed $p$-Laplacian is shown to outperform standard hypergraph Laplacians in the experiment on a hypergraph semi-supervised learning and normalized cut setting.
