This work considers the question of how convenient access to copious data impacts our
ability to learn causal effects and relations. In what ways is learning causality in the era of …

Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …

While reinforcement learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …

Structural causal bandit provides a framework for online decision-making problems when
causal information is available. It models the stochastic environment with a structural causal …

Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …

Learning good interventions in a causal graph can be modelled as a stochastic multi-armed
bandit problem with side-information. First, we study this problem when interventions are …

Abstract Machine learning (ML) models have been widely used to make predictions. Instead
of a predictive statement about future outcomes, in many situations we want to pursue a …

This paper studies the problem of designing an optimal sequence of interventions in a
causal graphical model to minimize cumulative regret with respect to the best intervention in …

Multi-armed bandit problems provide a framework to identify the optimal intervention over a
sequence of repeated experiments. Without additional assumptions, minimax optimal …

Transforming a causal system from a given initial state to a desired target state is an
important task permeating multiple fields including control theory, biology, and materials …