The University of Tokyo · 컴퓨터과학
마사시 수기야마 교수의 연구실은 주로 비정상성 환경, 특히 입력 분포의 변화가 발생하는 '공변량 이동(Covariate Shift)' 문제에 초점을 맞추고 있습니다. 이는 이미지 인식, 자연어 처리, 로봇 제어 등 실제 응용에서 흔히 발생하는 데이터 분포의 변화에 대응하기 위한 학습 방법을 개발하는 데 목적이 있습니다. 특히 밀도 비율 추정 기반의 새로운 접근법을 통해 차원 축소, 이상치 탐지, 조건부 밀도 추정 등 다양한 머신러닝 문제를 통합적으로 해결하는 데 기여하고 있습니다. 연구는 이론적 분석과 실용적 응용을 동시에 고려하여, 실제 데이터 환경에서의 안정성과 일반화 성능 향상을 추구합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Reducing the dimensionality of data without losing intrinsic information is an important preprocessing step in high-dimensional data analysis. Fisher discriminant analysis (FDA) is a traditional technique for supervised dimensionality reduction, but it tends to give undesired results if samples in a class are multimodal. An unsupervised dimensionality reduction method called localitypreserving projection (LPP) can work well with multimodal data due to its locality preserving property. However, s
A common assumption in supervised learning is that the input points in the training set follow the same probability distribution as the input points that will be given in the future test phase. However, this assumption is not satisfied, for example, when the outside of the training region is extrapolated. The situation where the training input points and test input points follow different distributions while the conditional distribution of output values given input points is unchanged is called
A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likelihood estimation are no longer consistent-weighted variants according to the ratio of test and training input densities are consistent. Therefore, accurately estimating the density ratio, called the importance, is one of the key issues in covariate shift adaptation. A naive approach to this task is to first estimate trai
Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditi
Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper, we consider the supervised dimensionality reduction problem where samples are accompanied with class labels. Traditional Fisher discriminant analysis is a popular and powerful method for this purpose. However, it tends to give undesired results if samples in some class form several separate clusters, i.e., multimodal. In this paper, we propose a new dimensionality reduction meth
As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is o
Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift” non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech reco
The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this letter, we consider a feature-wise kernelized Lasso for capturing nonlinear input-output dependency. We first show that with particular choices of kernel functions, nonredundant feat
A common assumption in supervised learning is that the training and test input points follow the same probability distribution. However, this assumption is not fulfilled, e.g., in interpolation, extrapolation, active learning, or classification with imbalanced data. The violation of this assumption—known as the covariate shift —causes a heavy bias in standard generalization error estimation schemes such as cross-validation or Akaike's information criterion, and thus they result in poor model sel
The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in linear regression scenarios. Traditional active learning methods using least-squares learning often assume that the model used for learning is correctly specified. In many practical situations, however, this assumption may not be fulfilled. Recently, active learning methods using importance-weighted least-squares learning h
