50 citations · 153 across the 35 of their papers we have counts for
10 papers · 1 filter
Variable Splitting Binary Tree Models Based on Bayesian Context Tree Models for Time Series Segmentation
Yuta Nakahara, Shota Saito, Kohei Horinouchi +4
We propose a variable splitting binary tree (VSBT) model based on Bayesian context tree (BCT) models for time series segmentation. Unlike previous applications of BCT models, the t…
Soft Bayesian Context Tree Models for Real-Valued Time Series
Shota Saito, Yuta Nakahara, Toshiyasu Matsushima
This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers soft (probabilistic) splits of t…
Bandits with Abstention under Expert Advice
Stephen Pasteris, Alberto Rumi, Maximilian Thiessen +4
We study the classic problem of prediction with expert advice under bandit feedback. Our model assumes that one action, corresponding to the learner's abstention from play, has no…
Multi-class Graph Clustering via Approximated Effective -Resistance
Shota Saito, Mark Herbster
This paper develops an approximation to the (effective) -resistance and applies it to multi-class clustering. Spectral methods based on the graph Laplacian and its generalizatio…
Prediction Algorithms Achieving Bayesian Decision Theoretical Optimality Based on Decision Trees as Data Observation Processes
Yuta Nakahara, Shota Saito, Naoki Ichijo +2
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…
Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computing
Shoma Shimizu, Takayuki Nishio, Shota Saito +3
This paper proposes a neural architecture search (NAS) method for split computing. Split computing is an emerging machine-learning inference technique that addresses the privacy an…
Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel -means, and Heat Kernel
Shota Saito
We propose a theoretical framework of multi-way similarity to model real-valued data into hypergraphs for clustering via spectral embedding. For graph cut based spectral clustering…
Probability Distribution on Rooted Trees
Yuta Nakahara, Shota Saito, Akira Kamatsuka +1
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…
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari +3
High sensitivity of neural architecture search (NAS) methods against their input such as step-size (i.e., learning rate) and search space prevents practitioners from applying them…
Parameterless Stochastic Natural Gradient Method for Discrete Optimization and its Application to Hyper-Parameter Optimization for Neural Network
Kouhei Nishida, Hernan Aguirre, Shota Saito +2
Black box discrete optimization (BBDO) appears in wide range of engineering tasks. Evolutionary or other BBDO approaches have been applied, aiming at automating necessary tuning of…