Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Imaizumi, Masaaki"'
Diffusion models have demonstrated exceptional performances in various fields of generative modeling. While they often outperform competitors including VAEs and GANs in sample quality and diversity, they suffer from slow sampling speed due to their i
Externí odkaz:
http://arxiv.org/abs/2410.08709
Autor:
Okano, Ryo, Imaizumi, Masaaki
We develop a novel clustering method for distributional data, where each data point is regarded as a probability distribution on the real line. For distributional data, it has been challenging to develop a clustering method that utilizes the mode of
Externí odkaz:
http://arxiv.org/abs/2407.08228
We consider a variant of the stochastic gradient descent (SGD) with a random learning rate and reveal its convergence properties. SGD is a widely used stochastic optimization algorithm in machine learning, especially deep learning. Numerous studies r
Externí odkaz:
http://arxiv.org/abs/2406.16032
Selecting or designing an appropriate domain adaptation algorithm for a given problem remains challenging. This paper presents a Transformer model that can provably approximate and opt for domain adaptation methods for a given dataset in the in-conte
Externí odkaz:
http://arxiv.org/abs/2405.16819
This study proposes a novel method for estimation and hypothesis testing in high-dimensional single-index models. We address a common scenario where the sample size and the dimension of regression coefficients are large and comparable. Unlike traditi
Externí odkaz:
http://arxiv.org/abs/2404.17812
This paper examines the quantization methods used in large-scale data analysis models and their hyperparameter choices. The recent surge in data analysis scale has significantly increased computational resource requirements. To address this, quantizi
Externí odkaz:
http://arxiv.org/abs/2401.17269
Autor:
Kato, Masahiro, Imaizumi, Masaaki
In causal inference about two treatments, Conditional Average Treatment Effects (CATEs) play an important role as a quantity representing an individualized causal effect, defined as a difference between the expected outcomes of the two treatments con
Externí odkaz:
http://arxiv.org/abs/2310.16819
Synthetic Control Methods (SCMs) have become an essential tool for comparative case studies. The fundamental idea of SCMs is to estimate the counterfactual outcomes of a treated unit using a weighted sum of the observed outcomes of untreated units. T
Externí odkaz:
http://arxiv.org/abs/2307.11127
Publikováno v:
Electronic Journal of Statistics 2024, Vol. 18, No. 1, 515-552
We develop a statistical inference method for an optimal transport map between distributions on real numbers with uniform confidence bands. The concept of optimal transport (OT) is used to measure distances between distributions, and OT maps are used
Externí odkaz:
http://arxiv.org/abs/2307.09257
Autor:
Okano, Ryo, Imaizumi, Masaaki
Distribution data refers to a data set where each sample is represented as a probability distribution, a subject area receiving burgeoning interest in the field of statistics. Although several studies have developed distribution-to-distribution regre
Externí odkaz:
http://arxiv.org/abs/2307.06137