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Found 20 papers, 6 papers with code
Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information
We propose a novel algorithm for multi-player multi-armed bandits without collision sensing information.
A High Performance, Low Complexity Algorithm for Multi-Player Bandits Without Collision Sensing Information
Motivated by applications in cognitive radio networks, we consider the decentralized multi-player multi-armed bandit problem, without collision nor sensing information.
Asymptotically Optimal Strategies For Combinatorial Semi-Bandits in Polynomial Time
We consider combinatorial semi-bandits with uncorrelated Gaussian rewards.
On the Suboptimality of Thompson Sampling in High Dimensions
In this paper we consider Thompson Sampling (TS) for combinatorial semi-bandits.
Solving Random Parity Games in Polynomial Time
We further propose the SWCP (Self-Winning Cycles Propagation) algorithm and show that, when the degree is large enough, SWCP solves the game with high probability.
Statistically Efficient, Polynomial Time Algorithms for Combinatorial Semi Bandits
We consider combinatorial semi-bandits over a set of arms ${\cal X} \subset \{0, 1\}^d$ where rewards are uncorrelated across items.
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling
Stochastic Rank-One Bandits (Katarya et al, (2017a, b)) are a simple framework for regret minimization problems over rank-one matrices of arms.
Computationally Efficient Estimation of the Spectral Gap of a Markov Chain
We consider the problem of estimating from sample paths the absolute spectral gap $\gamma_*$ of a reversible, irreducible and aperiodic Markov chain $(X_t)_{t \in \mathbb{N}}$ over a finite state space $\Omega$.
Minimal Exploration in Structured Stochastic Bandits
This paper introduces and addresses a wide class of stochastic bandit problems where the function mapping the arm to the corresponding reward exhibits some known structural properties.
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
This implies that classical approaches cannot guarantee a non-trivial regret bound.
A Minimax Optimal Algorithm for Crowdsourcing
We further propose Triangular Estimation (TE), an algorithm for estimating the reliability of workers.
A Streaming Algorithm for Crowdsourced Data Classification
We propose a streaming algorithm for the binary classification of data based on crowdsourcing.
An extension of McDiarmid's inequality
We generalize McDiarmid's inequality for functions with bounded differences on a high probability set, using an extension argument.
Combinatorial Bandits Revisited
In the adversarial setting under bandit feedback, we propose \textsc{CombEXP}, an algorithm with the same regret scaling as state-of-the-art algorithms, but with lower computational complexity for some combinatorial problems.
Unimodal Bandits without Smoothness
To our knowledge, the SP algorithm constitutes the first sequential arm selection rule that achieves a regret and optimization error scaling as $O(\sqrt{T})$ and $O(1/\sqrt{T})$, respectively, up to a logarithmic factor for non-smooth expected reward functions, as well as for smooth functions with unknown smoothness.
Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms
We also provide a regret upper bound for OSUB in non-stationary environments where the expected rewards smoothly evolve over time.
Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms
For discrete Lipschitz bandits, we derive asymptotic problem specific lower bounds for the regret satisfied by any algorithm, and propose OSLB and CKL-UCB, two algorithms that efficiently exploit the Lipschitz structure of the problem.
Dynamic Rate and Channel Selection in Cognitive Radio Systems
In turn, the proposed algorithms optimally exploit the inherent structure of the throughput.
Stochastic Online Shortest Path Routing: The Value of Feedback
The parameters, and hence the optimal path, can only be estimated by routing packets through the network and observing the realized delays.
The association problem in wireless networks: a Policy Gradient Reinforcement Learning approach
The purpose of this paper is to develop a self-optimized association algorithm based on PGRL (Policy Gradient Reinforcement Learning), which is both scalable, stable and robust.
