A survey of methods for time series change point detection
S Aminikhanghahi, DJ Cook - Knowledge and information systems, 2017 - Springer
Change points are abrupt variations in time series data. Such abrupt changes may represent
transitions that occur between states. Detection of change points is useful in modelling and …
transitions that occur between states. Detection of change points is useful in modelling and …
Density-ratio matching under the bregman divergence: a unified framework of density-ratio estimation
Estimation of the ratio of probability densities has attracted a great deal of attention since it
can be used for addressing various statistical paradigms. A naive approach to density-ratio …
can be used for addressing various statistical paradigms. A naive approach to density-ratio …
Concrete problems in AI safety
Rapid progress in machine learning and artificial intelligence (AI) has brought increasing
attention to the potential impacts of AI technologies on society. In this paper we discuss one …
attention to the potential impacts of AI technologies on society. In this paper we discuss one …
Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle
The unprecedented challenges caused by the COVID-19 pandemic demand timely action.
However, due to the complex nature of policy making, a lag may exist between the time a …
However, due to the complex nature of policy making, a lag may exist between the time a …
Domain adaptation via transfer component analysis
Domain adaptation allows knowledge from a source domain to be transferred to a different
but related target domain. Intuitively, discovering a good feature representation across …
but related target domain. Intuitively, discovering a good feature representation across …
A nonparametric approach for multiple change point analysis of multivariate data
DS Matteson, NA James - Journal of the American Statistical …, 2014 - Taylor & Francis
Change point analysis has applications in a wide variety of fields. The general problem
concerns the inference of a change in distribution for a set of time-ordered observations …
concerns the inference of a change in distribution for a set of time-ordered observations …
Change-point detection in time-series data by relative density-ratio estimation
The objective of change-point detection is to discover abrupt property changes lying behind
time-series data. In this paper, we present a novel statistical change-point detection …
time-series data. In this paper, we present a novel statistical change-point detection …
[图书][B] Machine learning in non-stationary environments: Introduction to covariate shift adaptation
M Sugiyama, M Kawanabe - 2012 - books.google.com
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 …
shift" non-stationarity. As the power of computing has grown over the past few decades, the …
A discriminative framework for anomaly detection in large videos
We address an anomaly detection setting in which training sequences are unavailable and
anomalies are scored independently of temporal ordering. Current algorithms in anomaly …
anomalies are scored independently of temporal ordering. Current algorithms in anomaly …
[图书][B] Introduction to statistical machine learning
M Sugiyama - 2015 - books.google.com
Machine learning allows computers to learn and discern patterns without actually being
programmed. When Statistical techniques and machine learning are combined together they …
programmed. When Statistical techniques and machine learning are combined together they …