Contextual bandits are a rich model for sequential decision making given side information,
with important applications, eg, in recommender systems. We propose novel algorithms for …

How should we intervene on an unknown structural equation model to maximize a
downstream variable of interest? This setting, also known as causal Bayesian optimization …

Computational tractability and social welfare (aka. efficiency) of equilibria are two
fundamental but in general orthogonal considerations in algorithmic game theory …

Contextual Bayesian optimization (CBO) is a powerful framework for sequential decision-
making given side information, with important applications, eg, in wind energy systems. In …

Systems of competing agents can often be modeled as games. Assuming rationality, the
most likely outcomes are given by an equilibrium, eg, a Nash equilibrium. In many practical …

Anomaly detection plays an essential role in network security and traffic classification. Many
studies have focused on anomaly detection to improve network security, including machine …

Abstract Systems of interacting agents can often be modeled as contextual games, where
the context encodes additional information, beyond the control of any agent (eg weather for …

We consider model-based multi-agent reinforcement learning, where the environment
transition model is unknown and can only be learned via expensive interactions with the …

Recently, remarkable progress has been made by approximating Nash equilibrium (NE),
correlated equilibrium (CE), and coarse correlated equilibrium (CCE) through function …

In Causal Bayesian Optimization (CBO), an agent intervenes on a structural causal model
with known graph but unknown mechanisms to maximize a downstream reward variable. In …