Linear Contextual Bandits with Interference
Interference, a key concept in causal inference, extends the reward modeling process by accounting for the impact of one unit's actions on the rewards of others. In contextual bandit (CB) settings, where multiple units are present in the same round, potential interference can significantly affect the estimation of expected rewards for different arms, thereby influencing the decision-making process. Although some prior work has explored multi-agent and adversarial bandits in interference-aware settings, the effect of interference in CB, as well as the underlying theory, remains significantly underexplored. In this paper, we introduce a systematic framework to address interference in Linear CB (LinCB), bridging the gap between causal inference and online decision-making. We propose a series of algorithms that explicitly quantify the interference effect in the reward modeling process and provide comprehensive theoretical guarantees, including sublinear regret bounds, finite sample upper bounds, and asymptotic properties. The effectiveness of our approach is demonstrated through simulations and a synthetic data generated based on MovieLens data.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal InferenceDecision MakingMulti-Armed BanditsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Learning in Generalized Linear Contextual Bandits with Stochastic Delays
In this paper, we consider online learning in generalized linear contextual bandits where rewards are not immediately observed. Instead, rewards are available to the decision maker only after some delay, which is unknown…
Multi-Armed BanditsContexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms
In this paper, we address the stochastic contextual linear bandit problem, where a decision maker is provided a context (a random set of actions drawn from a distribution). The expected reward of each action is specified…
Multi-Armed BanditsContextual Bandits with Random Projection
Contextual bandits with linear payoffs, which are also known as linear bandits, provide a powerful alternative for solving practical problems of sequential decisions, e.g., online advertisements. In the era of big data, …
Multi-Armed BanditsA conversion theorem and minimax optimality for continuum contextual bandits
We study the contextual continuum bandits problem, where the learner sequentially receives a side information vector and has to choose an action in a convex set, minimizing a function associated with the context. The goa…
Multi-Armed BanditsProvably Optimal Algorithms for Generalized Linear Contextual Bandits
Contextual bandits are widely used in Internet services from news recommendation to advertising, and to Web search. Generalized linear models (logistical regression in particular) have demonstrated stronger performance t…
Multi-Armed BanditsNews Recommendation