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4 results for “contextual bandit”
Datasets from the RecSys 2020 article "Carousel Personalization in Music Streaming Apps with Contextual Bandits"
<p>We publicly release the anonymized <em>user_features.csv</em> and <em>playlist_features.csv</em> datasets, from the music streaming platform Deezer, as described in the article "<em>Carousel Personalization in Music Streaming Apps with Contextual Bandits"</em> published in the proceedings of the 14th ACM Conference on Recommender Systems (<em>RecSys 2020</em>). The paper is available <a href="https://arxiv.org/abs/2009.06546">here</a>.</p> <p>These datasets are used in the GitHub repository <a href="https://github.com/deezer/carousel_bandits">deezer/carousel_bandits</a> to reproduce experiments from the article.</p> <p>Please cite our paper if you use our code or data in your work.</p>
Experimental data analyzed in: Signal detection models as contextual bandits
<p>Signal detection theory (SDT) has been widely applied to identify the optimal discriminative decisions of receivers under uncertainty. However, the approach assumes that decision-makers immediately adopt the appropriate acceptance threshold, even though the optimal response must often be learned. Here we recast the classical normal-normal (and power-law) signal detection model as a contextual multi-armed bandit (CMAB). Thus, rather than starting with complete information, decision-makers must infer how the magnitude of a continuous cue is related to the probability that a signaller is desirable, while simultaneously seeking to exploit the information they acquire. We explain how various CMAB heuristics resolve the trade-off between better estimating the underlying relationship and exploiting it. Next, we determined how naïve human volunteers resolve signal detection problems with a continuous cue. As anticipated, a model of choice (accept/reject) that assumed volunteers immediately adopted the SDT-predicted acceptance threshold did not predict volunteer behaviour well. The Softmax rule for solving CMABs, with choices based on a logistic function of the expected payoffs, best explained the decisions of our volunteers but a simple midpoint algorithm also predicted decisions well under some conditions. CMABs offer principled parametric solutions to solving many classical SDT problems when decision-makers start with incomplete information.</p>
Experimental data analyzed in: Signal detection models as contextual bandits
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Data used in real world-inspired experiments for submission titled "Causal Contextual Bandits with Targeted Interventions"
<p>Data used in real world-inspired experiments for submission titled "Causal Contextual Bandits with Targeted Interventions".</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.