mlsanityData to 7 Oct 2026

Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits

arXiv 2410.02068 ↗First posted 2 Oct 2024cs.LG stat.MLcited by 6

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Abstract (arXiv)

We study how representation learning can improve the learning efficiency of contextual bandit problems. We study the setting where we play T contextual linear bandits with dimension d simultaneously, and these T bandit tasks collectively share a common linear representation with a dimensionality of r much smaller than d. We present a new algorithm based on alternating projected gradient descent (GD) and minimization estimator to recover a low-rank feature matrix. Using the proposed estimator, we present a multi-task learning algorithm for linear contextual bandits and prove the regret bound of our algorithm. We presented experiments and compared the performance of our algorithm against benchmark algorithms.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 24 of its 50 references that are in the map. First posted on arXiv 2 Oct 2024.

Builds on

24 of 50 references.

References · 24

  1. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  2. 2020Few-Shot Learning via Learning the Representation, Provablycited by 213
  3. 2015The Benefit of Multitask Representation Learningcited by 194
  4. 2015Actor-Mimic: Deep Multitask and Transfer Reinforcement Learningcited by 176
  5. 2020Provable Meta-Learning of Linear Representationscited by 161
  6. 2020Spectral Methods for Data Science: A Statistical Perspectivecited by 111
  7. 2019Batched Multi-armed Bandits Problemcited by 97
  8. 2020Sequential Batch Learning in Finite-Action Linear Contextual Banditscited by 63
  9. 2020High-Dimensional Sparse Linear Banditscited by 61
  10. 2019Bilinear Bandits with Low-rank Structurecited by 54
  11. 2020Impact of Representation Learning in Linear Banditscited by 51
  12. 2020Low-Rank Generalized Linear Bandit Problemscited by 48
12 more
  1. 2017Multi-Task Learning for Contextual Banditscited by 47
  2. 2021Near-optimal Representation Learning for Linear Bandits and Linear RLcited by 45
  3. 2024Sharing Knowledge in Multi-Task Deep Reinforcement Learningcited by 42
  4. 2020Provable Representation Learning for Imitation Learning via Bi-level Optimizationcited by 41
  5. 2016Fast Algorithms for Robust PCA via Gradient Descentcited by 39
  6. 2017Stochastic Low-Rank Banditscited by 28
  7. 2021Sample Efficient Linear Meta-Learning by Alternating Minimizationcited by 25
  8. 2021Information Directed Sampling for Sparse Linear Banditscited by 20
  9. 2022Multi-task Representation Learning with Stochastic Linear Banditscited by 20
  10. 2019Stochastic Linear Bandits with Hidden Low Rank Structurecited by 14
  11. 2015Distributed Multitask Learningcited by 11
  12. 2024Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraintscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025Byzantine Resilient Federated Multi-Task Representation Learningcited by 1
  2. 2025AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimizationcited by 0
  3. 2025Beyond Centralization: Provable Communication Efficient Decentralized Multi-Task Learningcited by 0
  4. 2025Diffusion-based Decentralized Federated Multi-Task Representation Learningcited by 0
  5. 2026RepUCB: Representation Learning-Based UCB for Heterogeneous Multi-Task Linear Banditscited by 0
  6. 2026Multi-Task Representation Learning for Conservative Linear Banditscited by 0