Seminars

Statistical inference from high-dimensional data with low-dimensional structures has recently attracted a lot of attention. In machine learning, deep generative…

Although distributional reinforcement learning (DRL) has been widely examined in the past few years, very few studies investigate the validity…

Rapid data science developments and the desire to have interpretable AI require us to have innovative frameworks to tackle frequently…

The increasing availability of electronic health records (EHR) data offers valuable prospects for biomedical research, enhancing patient care. However, leveraging…

The concept of conditional independence allows us to distinguish between direct and indirect associations in data and thus has a…

Sampling in high-dimensional spaces presents a considerable challenge in various areas of statistical computation. Traditional methods like Markov Chain Monte…

As we are entering the big data era with technological advances of data collection, high-dimensional and complex data is becoming…

How can we conduct inference on high-dimensional data when classical asymptotic theory fails because noise accumulates faster than at root-n…

In contemporary research, there exists a wide range of theories and techniques that can be utilized to estimate causal effects…

In the realm of classical statistics, the focus has traditionally been on handling observations that can be represented as real…