50 citations · 153 across the 35 of their papers we have counts for
37 papers
Anomalies and discrete torsion in Type II and Type I worldsheet theories
Masashi Kawahira, Shotaro Kawanago, Shota Saito +1
We provide a comprehensive study of anomalies and discrete torsion in the worldsheet theories of Type II and Type I string theories on a general ten-dimensional target space. In th…
AMSB in Truly Confining Gauge Theories
Riku Ishikawa, Hitoshi Murayama, Shota Saito
We study deformation of supersymmetric -confining (truly confininig) gauge theories with small anomaly mediation of supersymmetry breaking. We identify breaking pattern of globa…
Bosonic SPT and invertible phases and its relation to Steenrod's problem
Shota Saito, Yuji Tachikawa, Yi Zhang
Bosonic invertible and symmetry-protected topological (SPT) phases are well-known to be described by ordinary cohomology groups in low dimensions, but `beyond-cohomology' phases ap…
A Mixture Autoregressive Image Generative Model on Quadtree Regions for Gaussian Noise Removal via Variational Bayes and Gradient Methods
Shota Saito, Yuta Nakahara, Kohei Horinouchi +3
This paper addresses the problem of image denoising for grayscale images. We propose a probabilistic image generative model that combines a quadtree region-partitioning model with…
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…
Soft Guessing Under Logarithmic Loss Allowing Errors and Variable-Length Source Coding
Shota Saito, Hamdi Joudeh
This paper considers the problem of soft guessing under a logarithmic loss distortion measure while allowing errors. We find an optimal guessing strategy, and derive single-shot up…
Truly Confining Supersymmetric Gauge Theories
Riku Ishikawa, Hitoshi Murayama, Shota Saito
We classify ``truly confining'' (t-confining) supersymmetric gauge theories, in which no center charges can be screened, and Wilson loops in the fundamental representation are ther…
CatCMA with Margin for Single- and Multi-Objective Mixed-Variable Black-Box Optimization
Ryoki Hamano, Masahiro Nomura, Shota Saito +2
This study focuses on mixed-variable black-box optimization (MV-BBO), addressing continuous, integer, and categorical variables. Many real-world MV-BBO problems involve dependencie…
Cancelling mod-2 anomalies by Green-Schwarz mechanism with
Shota Saito, Yuji Tachikawa
We study if and when mod-2 anomalies can be canceled by the Green-Schwarz mechanism with the introduction of an antisymmetric tensor field . As explicit examples, we examin…
CMA-ES for Discrete and Mixed-Variable Optimization on Sets of Points
Kento Uchida, Ryoki Hamano, Masahiro Nomura +2
Discrete and mixed-variable optimization problems have appeared in several real-world applications. Most of the research on mixed-variable optimization considers a mixture of integ…
Impurity contribution to ultraviolet absorption of saturated fatty acids
Shota Saito, Naoki Numadate, Hidemasa Teraoka +3
Saturated fatty acids are abundant organic compounds in oceans and sea sprays. Their photochemical reactions induced by solar radiation have recently been discovered as an abiotic…
CMA-ES for Safe Optimization
Kento Uchida, Ryoki Hamano, Masahiro Nomura +2
In several real-world applications in medical and control engineering, there are unsafe solutions whose evaluations involve inherent risk. This optimization setting is known as saf…
CatCMA : Stochastic Optimization for Mixed-Category Problems
Ryoki Hamano, Shota Saito, Masahiro Nomura +2
Black-box optimization problems often require simultaneously optimizing different types of variables, such as continuous, integer, and categorical variables. Unlike integer variabl…
Wess-Zumino-Witten Terms of QCD by Bordism Theory
Shota Saito
We investigate the four-dimensional Wess-Zumino-Witten (WZW) terms within the framework of quantum chromodynamics (QCD) using invertible field theory through bordism theory. W…
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…
Soft Guessing Under Log-Loss Distortion Allowing Errors
Shota Saito
This paper deals with the problem of soft guessing under log-loss distortion (logarithmic loss) that was recently investigated by [Wu and Joudeh, IEEE ISIT, pp. 466--471, 2023]. We…
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…
(1+1)-CMA-ES with Margin for Discrete and Mixed-Integer Problems
Yohei Watanabe, Kento Uchida, Ryoki Hamano +3
The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient continuous black-box optimization method. The CMA-ES possesses many attractive features, including inva…
Marginal Probability-Based Integer Handling for CMA-ES Tackling Single-and Multi-Objective Mixed-Integer Black-Box Optimization
Ryoki Hamano, Shota Saito, Masahiro Nomura +1
This study targets the mixed-integer black-box optimization (MI-BBO) problem where continuous and integer variables should be optimized simultaneously. The CMA-ES, our focus in thi…
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…
Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling
Yuhei Noda, Shota Saito, Shinichi Shirakawa
Neural architecture search (NAS) aims to automate architecture design processes and improve the performance of deep neural networks. Platform-aware NAS methods consider both perfor…
CMA-ES with Margin: Lower-Bounding Marginal Probability for Mixed-Integer Black-Box Optimization
Ryoki Hamano, Shota Saito, Masahiro Nomura +1
This study targets the mixed-integer black-box optimization (MI-BBO) problem where continuous and integer variables should be optimized simultaneously. The CMA-ES, our focus in thi…