Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

Regret minimization in streaming multi-armed bandits (MABs) has been studied extensively,
and recent work has shown that algorithms with $ o (K) $ memory have to incur $\Omega …

Contextual bandits often provide simple and effective personalization in decision making
problems, making them popular tools to deliver personalized interventions in mobile health …

This paper considers a stochastic Multi-Armed Bandit (MAB) problem with dual objectives:(i)
quick identification and commitment to the optimal arm, and (ii) reward maximization …

Adaptive experimental design methods are increasingly being used in industry as a tool to
boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing …

Real-time bidding systems, which utilize auctions to allocate user impressions to competing
advertisers, continue to enjoy success in digital advertising. Assessing the effectiveness of …

We study the Pareto frontier of two archetypal objectives in multi-armed bandits, namely,
regret minimization (RM) and best arm identification (BAI) with a fixed horizon. It is folklore …

Practitioners conducting adaptive experiments often encounter two competing priorities:
reducing the cost of experimentation by effectively assigning treatments during the …

In this paper, we study reinforcement learning from human feedback (RLHF) under an
episodic Markov decision process with a general trajectory-wise reward model. We …

This thesis presents contributions to the problem of learning from logged interactions using
the offline contextual bandit framework. We are interested in two related topics:(1) offline …