Meta learning is a promising paradigm to enable skill transfer across tasks. Most previous
methods employ the empirical risk minimization principle in optimization. However, the …

Offline reinforcement learning (RL) is suitable for safety-critical domains where online
exploration is not feasible. In such domains, decision-making should take into consideration …

Reinforcement learning (RL) presents numerous benefits compared to rule-based
approaches in various applications. Privacy concerns have grown with the widespread use …

Risk occupies a central role in both the theory and practice of decision-making. Although it is
deeply implicated in many conditions involving dysfunctional behavior and thought, modern …

After a risky choice, decision makers must frequently wait out a delay period before the
outcome of their choice becomes known. In contemporary sports-betting apps, decision …

Many sequential decision-making problems that are currently automated, such as those in
manufacturing or recommender systems, operate in an environment where there is either …

In the classical Reinforcement Learning (RL) setting, one aims to find a policy that
maximizes its expected return. This objective may be inappropriate in safety-critical domains …

Epistemic uncertainty, which stems from what a learning algorithm does not know, is the
natural signal for exploration. Capturing and exploiting epistemic uncertainty for efficient …

Attitudes towards risk play a crucial role in everyday decision-making as well as in
psychiatric disorders like anxiety. The Balloon Analogue Risk Task (BART) provides a …

Conditional value-at-risk (CVaR) precisely characterizes the influence that rare, catastrophic
events can exert over decisions. Such characterizations are important for both normal …