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Non stationary multi-armed bandit: Empirical evaluation of a new concept drift-aware algorithm
The Multi-Armed Bandit (MAB) problem has been extensively studied in order to address
real-world challenges related to sequential decision making. In this setting, an agent selects …
real-world challenges related to sequential decision making. In this setting, an agent selects …
A definition of non-stationary bandits
Despite the subject of non-stationary bandit learning having attracted much recent attention,
we have yet to identify a formal definition of non-stationarity that can consistently distinguish …
we have yet to identify a formal definition of non-stationarity that can consistently distinguish …
Learning and optimization with seasonal patterns
A standard assumption adopted in the multiarmed bandit (MAB) framework is that the mean
rewards are constant over time. This assumption can be restrictive in the business world as …
rewards are constant over time. This assumption can be restrictive in the business world as …
Linear bandits with memory: from rotting to rising
Nonstationary phenomena, such as satiation effects in recommendations, have mostly been
modeled using bandits with finitely many arms. However, the richer action space provided …
modeled using bandits with finitely many arms. However, the richer action space provided …
Non-Stationary Bandits with Periodic Behavior: Harnessing Ramanujan Periodicity Transforms to Conquer Time-Varying Challenges
In traditional multi-armed bandits (MAB), a standard assumption is that the mean rewards
are constant across each arm, a simplification that can be restrictive in nature. In many real …
are constant across each arm, a simplification that can be restrictive in nature. In many real …
A Survey on Techniques and Methods of Recommender System
As prevalence is growing for social media, the value of its content is becoming
paramounting. This data can reveal about a person's personal and professional life. The …
paramounting. This data can reveal about a person's personal and professional life. The …
Linear Bandits with Memory
Nonstationary phenomena, such as satiation effects in recommendations, have mostly been
modeled using bandits with finitely many arms. However, the richer action space provided …
modeled using bandits with finitely many arms. However, the richer action space provided …
Data Efficient Sequential Decision Making in High Dimensions
VS Gattani - 2024 - search.proquest.com
As machine learning (ML) systems rapidly advance, their scale and data requirements have
surged, increasing the need for efficient data use while maintaining high performance and …
surged, increasing the need for efficient data use while maintaining high performance and …
Lifelong Learning in Multi-Armed Bandits
Continuously learning and leveraging the knowledge accumulated from prior tasks in order
to improve future performance is a long standing machine learning problem. In this paper …
to improve future performance is a long standing machine learning problem. In this paper …
Sequential decision problems in non-stationary environments
Y Russac - 2022 - theses.hal.science
The vanilla bandit model assumes thatthe rewards are independent andidentically
distributed. However, this assumption is restrictive: it prevents from modelingevolving …
distributed. However, this assumption is restrictive: it prevents from modelingevolving …