PFN-TS converts PFN posterior predictives into mean-reward samples for Thompson sampling using a subsampled predictive CLT, with consistency proofs, regret bounds, and strong empirical performance on synthetic and real bandit benchmarks.
ISBN 978-1-4503-0493-1
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Eye-tracking study shows F-pattern and examination hypothesis from web search do not hold in carousel interfaces; users follow an L-pattern on clicks, ignore headings, and examination does not predict clicks as assumed.
Controlled personalization combining editorial curation with modest algorithmic recommendations in legacy news increases engagement, diversity, and reduces popularity bias per an A/B test.
citing papers explorer
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PFN-TS: Thompson Sampling for Contextual Bandits via Prior-Data Fitted Networks
PFN-TS converts PFN posterior predictives into mean-reward samples for Thompson sampling using a subsampled predictive CLT, with consistency proofs, regret bounds, and strong empirical performance on synthetic and real bandit benchmarks.
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Following the Eye-Tracking Evidence: Established Web-Search Assumptions Fail in Carousel Interfaces
Eye-tracking study shows F-pattern and examination hypothesis from web search do not hold in carousel interfaces; users follow an L-pattern on clicks, ignore headings, and examination does not predict clicks as assumed.
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Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendation
Controlled personalization combining editorial curation with modest algorithmic recommendations in legacy news increases engagement, diversity, and reduces popularity bias per an A/B test.