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Found 6 papers, 0 papers with code
Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit
We study a robust multi-agent multi-armed bandit problem where multiple clients or participants are distributed on a fully decentralized blockchain, with the possibility of some being malicious.
Regret Lower Bounds in Multi-agent Multi-armed Bandit
Multi-armed Bandit motivates methods with provable upper bounds on regret and also the counterpart lower bounds have been extensively studied in this context.
Pareto Regret Analyses in Multi-objective Multi-armed Bandit
We study Pareto optimality in multi-objective multi-armed bandit by providing a formulation of adversarial multi-objective multi-armed bandit and defining its Pareto regrets that can be applied to both stochastic and adversarial settings.
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace
We show the effectiveness of GCF by deriving the asymptotic property of the estimator and comparing it to popular uplift modeling methods on both synthetic and real-world datasets.
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods
Heterogeneous treatment effect (HTE) estimation with continuous treatment is essential in multiple disciplines, such as the online marketplace and pharmaceutical industry.
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms
We propose a new algorithm, namely EXP4. P, by modifying EXP4 and establish its upper bound of regret in both bounded and unbounded sub-Gaussian contextual bandit settings.
