Results 91 to 100 of about 497,803 (209)

Nonparametric Stochastic Contextual Bandits

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
We analyze the K-armed bandit problem where the reward for each arm is a noisy realization based on an observed context under mild nonparametric assumptions.We attain tight results for top-arm identification and a sublinear regret of Õ(T1+D/(2+D), where D is the context dimension, for a modified UCB algorithm that is simple to implement.
Melody Y. Guan, Heinrich Jiang
openaire   +4 more sources

Balancing Efficiency and Efficacy: A Contextual Bandit-Driven Framework for Multi-Tier Cyber Threat Detection

Applied Sciences
In response to the rising volume and sophistication of cyber intrusions, data-oriented methods have emerged as critical defensive measures. While machine learning—including neural network-based solutions—has demonstrated strong capabilities in ...
Ibrahim Mutambik, Abdullah Almuqrin
doaj   +1 more source

SAMPLE-BASED DYNAMIC HIERARCHICAL TRANSFORMER WITH LAYER AND HEAD FLEXIBILITY VIA CONTEXTUAL BANDIT [PDF]

Proceedings on Engineering Sciences
Transformer requires a fixed number of layers and heads which makes them inflexible to the complexity of individual samples and expensive in training and inference.
Fanfei Meng , Lele Zhang , Yu Chen, Yuxin Wang   +3 more
doaj   +1 more source

Knowledge and Argument

Noûs, EarlyView.
ABSTRACT I argue that knowledge plays a distinctive role in psychological explanation that weaker epistemic states cannot because it is robust in the face of counterevidence in a way that they are not. Being robust in the face of counterevidence makes your belief robust in the face of counterargument.
Spencer Paulson
wiley   +1 more source

Dynamic Pricing Algorithm for Edge Computing Task Offloading Based on Contextual Multi-Armed Bandit [PDF]

Jisuanji gongcheng
Existing dynamic pricing algorithms for edge computing are based on game-theoretic models and auction mechanisms. With the optimization objective of maximizing a service provider′s total revenue, existing pricing algorithms face difficulties in obtaining
GAN Nan, FU Xiaodong, FENG Yan
doaj   +1 more source

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

Contextual Bandits with Random Projection

CoRR, 2019
Contextual bandits with linear payoffs, which are also known as linear bandits, provide a powerful alternative for solving practical problems of sequential decisions, e.g., online advertisements. In the era of big data, contextual data usually tend to be high-dimensional, which leads to new challenges for traditional linear bandits mostly designed for ...
openaire   +3 more sources

Scalable Neural Contextual Bandit for Recommender Systems [PDF]

, 2023
High-quality recommender systems ought to deliver both innovative and relevant content through effective and exploratory interactions with users. Yet, supervised learning-based neural networks, which form the backbone of many existing recommender systems,
Van Roy, Benjamin, Zhu, Zheqing
core   +1 more source

Transferable contextual bandit for cross-domain recommendation

, 2018
Traditional recommendation systems (RecSys) suffer from two problems: the exploitation-exploration dilemma and the cold-start problem. One solution to solving the exploitation-exploration dilemma is the contextual bandit policy, which adaptively exploits
Wei, Ying, Liu, Bo, Yan, Zhixian, Zhang, Yu, Yang, Qiang   +4 more
core   +2 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, Anna‐Lena Schubert, Willem E. Frankenhuis   +2 more
wiley   +1 more source