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Arindam Khan

7 accepted papers

2026

Ads that Stick: Near-Optimal Ad Optimization through Psychological Behavior Models

ICLR 2026poster

Optimizing the timing and frequency of advertisements (ads) is a central problem in digital advertising, with significant economic consequences. Existing scheduling policies rely on simple heuristics, such as uniform spacing and frequency caps, that overlook long-term user interest. However, it is w…

Cited by 0SourceScholar
2023

Fairness and Welfare Quantification for Regret in Multi-Armed Bandits

AAAI 2023technical

We extend the notion of regret with a welfarist perspective. Focussing on the classic multi-armed bandit (MAB) framework, the current work quantifies the performance of bandit algorithms by applying a fundamental welfare function, namely the Nash social welfare (NSW) function. This corresponds to eq…

Cited by 18SourcePDFScholar
2023

Finding Fair Allocations under Budget Constraints

AAAI 2023technical

We study the fair allocation of indivisible goods among agents with identical, additive valuations but individual budget constraints. Here, the indivisible goods--each with a specific size and value--need to be allocated such that the bundle assigned to each agent is of total size at most the agent'…

Cited by 21SourcePDFScholar
2023

Mitigating Disparity while Maximizing Reward: Tight Anytime Guarantee for Improving Bandits

IJCAI 2023poster

We study the Improving Multi-Armed Bandit problem, where the reward obtained from an arm increases with the number of pulls it receives. This model provides an elegant abstraction for many real-world problems in domains such as education and employment, where decisions about the distribution of oppo…

Cited by 3SourcePDFScholar
2022

Universal and Tight Online Algorithms for Generalized-Mean Welfare

AAAI 2022technical

We study fair and efficient allocation of divisible goods, in an online manner, among n agents. The goods arrive online in a sequence of T time periods. The agents' values for a good are revealed only after its arrival, and the online algorithm needs to fractionally allocate the good, immediately an…

Cited by 18SourcePDFScholar
2021

Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization

NeurIPS 2021poster

We study the Stochastic Multi-armed Bandit problem under bounded arm-memory. In this setting, the arms arrive in a stream, and the number of arms that can be stored in the memory at any time, is bounded. The decision-maker can only pull arms that are present in the memory. We address the problem f…

Cited by 19SourcePDFScholar