Results 51 to 60 of about 18,568,643 (214)
The Enemy Who Never Went Away: Memory, Nationalism and China's Japan Policy During the Cold War
Nations and Nationalism, EarlyView.ABSTRACT This study reassesses the relationship between war memory, nationalism and Sino–Japanese relations during the Cold War. Contrary to the dominant view in existing scholarship that China's nationalist remembering of the war emerged during the 1980s and 1990s, this article, drawing on an extensive survey of the People's Daily, demonstrates that ...
Yi Wang
wiley +1 more source
Knapsack based optimal policies for budget-limited multi-armed bandits
, 2012 In budget-limited multi-armed bandit (MAB) problems, the learner’s actions are costly and constrained by a fixed budget. Consequently, an optimal exploitation policy may not be to pull the optimal arm repeatedly, as is the case in other variants of MAB ...
Tran-Thanh, Long +4 more
core +1 more source
IEEE Access, 2021 We extend the adversarial/non-stochastic multi-play multi-armed bandit (MPMAB) to the case where the number of arms to play is variable. The work is motivated by the fact that the resources allocated to scan different critical locations in an ...
Yiyang Wang, Neda Masoud
doaj +1 more source
Two-Armed Dirichlet Bandits with Discounting
The Annals of Statistics, 1994 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +2 more sources
Algorithmic Monoculture and Its Critics
Philosophical Perspectives, EarlyView.ABSTRACT Algorithmic decision‐making is replacing idiosyncratic human judgment in domains such as hiring, lending, and criminal justice. This shift promises increased consistency, but many scholars worry that it can go too far. They warn of the dangers of algorithmic monoculture, in which all decisions across a domain are made using a single algorithm.
Brian Hedden, Manish Raghavan
wiley +1 more source
Multi-Armed Bandits and Their Classical Algorithms: Strengths and Limitations [PDF]
ITM Web of ConferencesMulti-armed Bandits (MAB), which is the short form for multi- armed bandit, is playing a significant role in varied spaces including online advertising, recommendation system, clinical trials and medical decision- making and even in 5G. The model aims to
Wu Sichen
doaj +1 more source
Contributions to the "Two-Armed Bandit" Problem
The Annals of Mathematical Statistics, 1962 The Bayes sequential design is obtained for an optimization problem involving the choice of experiments. Given are experiments $A, B$, densities $p_1, p_2$, a positive integer $N$ and a number $\xi \varepsilon \lbrack 0, 1\rbrack$. A sequence of $N$ observations is to be made such that at each stage either $A$ or $B$ is observed, the loss being 1 if ...
openaire +3 more sources
Computational Modeling of Decision Making Enhances the Adversity Researcher's Toolbox
Topics in Cognitive Science, EarlyView.Abstract Over the past decades, there has been major progress in our understanding of how adversity influences cognitive abilities and strategies. However, most of this research is based on raw performance, such as response times and accuracy. These measures are informative about decision‐making outcomes but tell us little about cognitive processes. In
Stefan Vermeent +2 more
wiley +1 more source
A Comprehensive Review on Federated and Incremental Learning in Spatial Crowdsourcing Systems
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 4, December 2026.A comprehensive overview of Federated and Incremental Learning (FIL) integration in Spatial Crowdsourcing, highlighting privacy, security, adaptability, efficiency challenges, and future research directions. ABSTRACT Spatial Crowdsourcing (SC) coordinates workers and requesters through personal mobile devices to complete location‐dependent tasks in ...
Md Mujibur Rahman +3 more
wiley +1 more source
When can the two-armed bandit algorithm be trusted?
The Annals of Applied Probability, 2004 We investigate the asymptotic behavior of one version of the so-called two-armed bandit algorithm. It is an example of stochastic approximation procedure whose associated ODE has both a repulsive and an attractive equilibrium, at which the procedure is noiseless.
Lamberton, Damien +2 more
openaire +6 more sources