Local Clustering in Contextual Multi-Armed Bandits

• URL: http://arxiv.org/abs/2103.00063v3
• Date: Fri, 24 Mar 2023 15:05:00 GMT
• Title: Local Clustering in Contextual Multi-Armed Bandits
• Authors: Yikun Ban, Jingrui He
• Abstract summary: We study identifying user clusters in contextual multi-armed bandits (MAB) We propose a bandit algorithm, LOCB, embedded with local clustering procedure. We evaluate the proposed algorithm from various aspects, which outperforms state-of-the-art baselines.
• Score: 44.11480686973274
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We study identifying user clusters in contextual multi-armed bandits (MAB). Contextual MAB is an effective tool for many real applications, such as content recommendation and online advertisement. In practice, user dependency plays an essential role in the user's actions, and thus the rewards. Clustering similar users can improve the quality of reward estimation, which in turn leads to more effective content recommendation and targeted advertising. Different from traditional clustering settings, we cluster users based on the unknown bandit parameters, which will be estimated incrementally. In particular, we define the problem of cluster detection in contextual MAB, and propose a bandit algorithm, LOCB, embedded with local clustering procedure. And, we provide theoretical analysis about LOCB in terms of the correctness and efficiency of clustering and its regret bound. Finally, we evaluate the proposed algorithm from various aspects, which outperforms state-of-the-art baselines.

Related papers

• Meta Clustering of Neural Bandits [45.77505279698894]
  We study a new problem, Clustering of Neural Bandits, by extending previous work to the arbitrary reward function. We propose a novel algorithm called M-CNB, which utilizes a meta-learner to represent and rapidly adapt to dynamic clusters. In extensive experiments conducted in both recommendation and online classification scenarios, M-CNB outperforms SOTA baselines.
  arXiv  Detail & Related papers  (2024-08-10T16:09:51Z)
• A3S: A General Active Clustering Method with Pairwise Constraints [66.74627463101837]
  A3S features strategic active clustering adjustment on the initial cluster result, which is obtained by an adaptive clustering algorithm. In extensive experiments across diverse real-world datasets, A3S achieves desired results with significantly fewer human queries.
  arXiv  Detail & Related papers  (2024-07-14T13:37:03Z)
• A Machine Learning-Based Framework for Clustering Residential Electricity Load Profiles to Enhance Demand Response Programs [0.0]
  We present a novel machine learning based framework in order to achieve optimal load profiling through a real case study. In this paper, we present a novel machine learning based framework in order to achieve optimal load profiling through a real case study.
  arXiv  Detail & Related papers  (2023-10-31T11:23:26Z)
• Cluster-level Group Representativity Fairness in $k$-means Clustering [3.420467786581458]
  Clustering algorithms could generate clusters such that different groups are disadvantaged within different clusters. We develop a clustering algorithm, building upon the centroid clustering paradigm pioneered by classical algorithms. We show that our method is effective in enhancing cluster-level group representativity fairness significantly at low impact on cluster coherence.
  arXiv  Detail & Related papers  (2022-12-29T22:02:28Z)
• Robust Consensus Clustering and its Applications for Advertising Forecasting [18.242055675730253]
  We propose a novel algorithm -- robust consensus clustering that can find common ground truth among experts' opinions. We apply the proposed method to the real-world advertising campaign segmentation and forecasting tasks.
  arXiv  Detail & Related papers  (2022-12-27T21:49:04Z)
• A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments [54.172993875654015]
  The paper proposes a family of communication efficient methods for distributed learning in heterogeneous environments. One-shot approach, based on local computations at the users and a clustering based aggregation step at the server is shown to provide strong learning guarantees. For strongly convex problems it is shown that, as long as the number of data points per user is above a threshold, the proposed approach achieves order-optimal mean-squared error rates in terms of the sample size.
  arXiv  Detail & Related papers  (2022-09-22T09:04:10Z)
• Federated Online Clustering of Bandits [35.21933787486559]
  Contextual multi-armed bandit (MAB) is an important sequential decision-making problem in recommendation systems. We study the federated online clustering of bandit (FCLUB) problem, which aims to minimize the total regret while satisfying privacy and communication considerations.
  arXiv  Detail & Related papers  (2022-08-31T13:46:02Z)
• On the Convergence of Clustered Federated Learning [57.934295064030636]
  In a federated learning system, the clients, e.g. mobile devices and organization participants, usually have different personal preferences or behavior patterns. This paper proposes a novel weighted client-based clustered FL algorithm to leverage the client's group and each client in a unified optimization framework.
  arXiv  Detail & Related papers  (2022-02-13T02:39:19Z)
• Gradient Based Clustering [72.15857783681658]
  We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality. The approach is an iterative two step procedure (alternating between cluster assignment and cluster center updates) and is applicable to a wide range of functions.
  arXiv  Detail & Related papers  (2022-02-01T19:31:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.