This tutorial provides a gentle introduction to kernel density estimation (KDE) and recent
advances regarding confidence bands and geometric/topological features. We begin with a …

This paper is an attempt to bridge the conceptual gaps between researchers working on the
two widely used approaches based on positive definite kernels: Bayesian learning or …

We aim at constructing a high performance model for defect detection that detects unknown
anomalous patterns of an image without anomalous data. To this end, we propose a two …

The remarkable practical success of deep learning has revealed some major surprises from
a theoretical perspective. In particular, simple gradient methods easily find near-optimal …

In crowd counting, each training image contains multiple people, where each person is
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …

Heterogeneous effect estimation is crucial in causal inference, with applications across
medicine and social science. Many methods for estimating conditional average treatment …

Despite the great empirical success of deep reinforcement learning, its theoretical
foundation is less well understood. In this work, we make the first attempt to theoretically …

We present a two-stage framework for deep one-class classification. We first learn self-
supervised representations from one-class data, and then build one-class classifiers on …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

In order to deal with the curse of dimensionality in reinforcement learning (RL), it is common
practice to make parametric assumptions where values or policies are functions of some low …