504 research outputs found
Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue
Motivated by the observation that overexposure to unwanted marketing
activities leads to customer dissatisfaction, we consider a setting where a
platform offers a sequence of messages to its users and is penalized when users
abandon the platform due to marketing fatigue. We propose a novel sequential
choice model to capture multiple interactions taking place between the platform
and its user: Upon receiving a message, a user decides on one of the three
actions: accept the message, skip and receive the next message, or abandon the
platform. Based on user feedback, the platform dynamically learns users'
abandonment distribution and their valuations of messages to determine the
length of the sequence and the order of the messages, while maximizing the
cumulative payoff over a horizon of length T. We refer to this online learning
task as the sequential choice bandit problem. For the offline combinatorial
optimization problem, we show that an efficient polynomial-time algorithm
exists. For the online problem, we propose an algorithm that balances
exploration and exploitation, and characterize its regret bound. Lastly, we
demonstrate how to extend the model with user contexts to incorporate
personalization
Stochastic Linear Bandits Robust to Adversarial Attacks
We consider a stochastic linear bandit problem in which the rewards are not
only subject to random noise, but also adversarial attacks subject to a
suitable budget (i.e., an upper bound on the sum of corruption magnitudes
across the time horizon). We provide two variants of a Robust Phased
Elimination algorithm, one that knows and one that does not. Both variants
are shown to attain near-optimal regret in the non-corrupted case ,
while incurring additional additive terms respectively having a linear and
quadratic dependency on in general. We present algorithm independent lower
bounds showing that these additive terms are near-optimal. In addition, in a
contextual setting, we revisit a setup of diverse contexts, and show that a
simple greedy algorithm is provably robust with a near-optimal additive regret
term, despite performing no explicit exploration and not knowing
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