Dynamic Spectrum Access using Stochastic Multi-User Bandits

• URL: http://arxiv.org/abs/2101.04388v1
• Date: Tue, 12 Jan 2021 10:29:57 GMT
• Title: Dynamic Spectrum Access using Stochastic Multi-User Bandits
• Authors: Meghana Bande, Akshayaa Magesh, Venugopal V. Veeravalli
• Abstract summary: The proposed algorithm consists of an estimation phase and an allocation phase. It is shown that if every user adopts the algorithm, the system wide regret is order-optimal of order $O(log T)$ over a time-horizon of duration. The algorithm is extended to the dynamic case where the number of users in the system evolves over time, and is shown to lead to sub-linear regret.
• Score: 23.129398025477204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A stochastic multi-user multi-armed bandit framework is used to develop algorithms for uncoordinated spectrum access. In contrast to prior work, it is assumed that rewards can be non-zero even under collisions, thus allowing for the number of users to be greater than the number of channels. The proposed algorithm consists of an estimation phase and an allocation phase. It is shown that if every user adopts the algorithm, the system wide regret is order-optimal of order $O(\log T)$ over a time-horizon of duration $T$. The regret guarantees hold for both the cases where the number of users is greater than or less than the number of channels. The algorithm is extended to the dynamic case where the number of users in the system evolves over time, and is shown to lead to sub-linear regret.

Related papers

• Variance-Dependent Regret Bounds for Non-stationary Linear Bandits [52.872628573907434]
  We propose algorithms that utilize the variance of the reward distribution as well as the $B_K$, and show that they can achieve tighter regret upper bounds. We introduce two novel algorithms: Restarted Weighted$textOFUL+$ and Restarted $textSAVE+$. Notably, when the total variance $V_K$ is much smaller than $K$, our algorithms outperform previous state-of-the-art results on non-stationary linear bandits under different settings.
  arXiv  Detail & Related papers  (2024-03-15T23:36:55Z)
• Combinatorial Stochastic-Greedy Bandit [79.1700188160944]
  We propose a novelgreedy bandit (SGB) algorithm for multi-armed bandit problems when no extra information other than the joint reward of the selected set of $n$ arms at each time $tin [T]$ is observed. SGB adopts an optimized-explore-then-commit approach and is specifically designed for scenarios with a large set of base arms.
  arXiv  Detail & Related papers  (2023-12-13T11:08:25Z)
• Adversarial Bandits with Multi-User Delayed Feedback: Theory and Application [17.64363983613468]
  We formulate an adversarial MAB problem with multi-user delayed feedback and design a modified EXP3 algorithm MUD-EXP3. In this paper, we consider that the delayed feedback results are from multiple users and are unrestricted on internal distribution.
  arXiv  Detail & Related papers  (2023-10-17T12:08:15Z)
• User-Level Differential Privacy With Few Examples Per User [73.81862394073308]
  We consider the example-scarce regime, where each user has only a few examples, and obtain the following results. For approximate-DP, we give a generic transformation of any item-level DP algorithm to a user-level DP algorithm. We present a simple technique for adapting the exponential mechanism [McSherry, Talwar FOCS 2007] to the user-level setting.
  arXiv  Detail & Related papers  (2023-09-21T21:51:55Z)
• Modeling Attrition in Recommender Systems with Departing Bandits [84.85560764274399]
  We propose a novel multi-armed bandit setup that captures policy-dependent horizons. We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal. We then move forward to the more challenging case, where users are divided among two types.
  arXiv  Detail & Related papers  (2022-03-25T02:30:54Z)
• A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits [11.918230810566945]
  We study the non-stationary multi-armed bandit problem, where the reward statistics of each arm may change several times during the course of learning. We propose a method that achieves, in $K$-armed bandit problems, a near-optimal $widetilde O(sqrtK N(S+1))$ dynamic regret.
  arXiv  Detail & Related papers  (2022-01-17T17:23:56Z)
• Combinatorial Multi-armed Bandits for Resource Allocation [23.869080705146395]
  Motivating examples include allocating limited computing time or wireless spectrum bands to multiple users. Decision maker should learn the value of the resources allocated for each user from feedback on each user's received reward.
  arXiv  Detail & Related papers  (2021-05-10T13:55:30Z)
• Regret in Online Recommendation Systems [73.58127515175127]
  This paper proposes a theoretical analysis of recommendation systems in an online setting, where items are sequentially recommended to users over time. In each round, a user, randomly picked from a population of $m$ users, requests a recommendation. The decision-maker observes the user and selects an item from a catalogue of $n$ items. The performance of the recommendation algorithm is captured through its regret, considering as a reference an Oracle algorithm aware of these probabilities.
  arXiv  Detail & Related papers  (2020-10-23T12:48:35Z)
• An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling [83.48992319018147]
  We consider the contextual bandit problem, where a player sequentially makes decisions based on past observations to maximize the cumulative reward. A natural way to resolve this problem is to apply online gradient descent (SGD) so that the per-step time and memory complexity can be reduced to constant. In this work, we show that online SGD can be applied to the generalized linear bandit problem. The proposed SGD-TS algorithm, which uses a single-step SGD update to exploit past information, achieves $tildeO(sqrtT)$ regret with the total time complexity that
  arXiv  Detail & Related papers  (2020-06-07T01:12:39Z)

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.