Filters








1,982 Hits in 5.9 sec

Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation [article]

Yuta Saito, Shunsuke Aihara, Megumi Matsutani, Yusuke Narita
<span title="2021-10-26">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
With the goal of enabling realistic and reproducible OPE research, we present Open Bandit Dataset, a public logged bandit dataset collected on a large-scale fashion e-commerce platform, ZOZOTOWN.  ...  We also develop Python software called Open Bandit Pipeline to streamline and standardize the implementation of batch bandit algorithms and OPE.  ...  With the goal of enabling realistic and reproducible OPE research, we publicize the Open Bandit Dataset collected on a large-scale fashion e-commerce platform, ZOZOTOWN.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.07146v5">arXiv:2008.07146v5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4ao2vwf2qnafplo5u47636uqai">fatcat:4ao2vwf2qnafplo5u47636uqai</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210212072945/https://arxiv.org/pdf/2008.07146v3.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/22/7e/227e70741cccd26d1465081cdd9e5230eb549b07.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.07146v5" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Multi-Dueling Bandits and Their Application to Online Ranker Evaluation

Brian Brost, Yevgeny Seldin, Ingemar J. Cox, Christina Lioma
<span title="">2016</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6g37zvjwwrhv3dizi6ffue642m" style="color: black;">Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM &#39;16</a> </i> &nbsp;
We evaluate our algorithm on several standard large-scale online ranker evaluation datasets.  ...  It can be modeled by dueling bandits, a mathematical model for online learning under limited feedback from pairwise comparisons.  ...  We used these as our parameter settings for MDB for all other experiments. Datasets We compare the algorithms on four large-scale evaluation datasets summarised in Table 1 1 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2983323.2983659">doi:10.1145/2983323.2983659</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cikm/BrostSCL16.html">dblp:conf/cikm/BrostSCL16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lopvamobzjdrbnwxvhoktwuguq">fatcat:lopvamobzjdrbnwxvhoktwuguq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180719055647/http://discovery.ucl.ac.uk/1531445/1/Cox_CIKM2016.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a4/1d/a41d6bfbb9b957d8f30af305348b371f0a46f14c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2983323.2983659"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries [article]

Evan R. Sparks, Ameet Talwalkar, Michael J. Franklin, Michael I. Jordan, Tim Kraska
<span title="2015-03-08">2015</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
on large-scale problems.  ...  In this work, we build upon these recent efforts and propose an integrated PAQ planning architecture that combines advanced model search techniques, bandit resource allocation via runtime algorithm introspection  ...  Recht who provided valuable ideas about derivative-free optimization and feedback, and Shivaram Venkataraman, Peter Bailis, Alan Fekete, Dan Crankshaw, Sanjay Krishnan, Xinghao Pan, and Kevin Jamieson for  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1502.00068v2">arXiv:1502.00068v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/l5ane47jazgq7cm3w7wh5cmho4">fatcat:l5ane47jazgq7cm3w7wh5cmho4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200911142917/https://arxiv.org/pdf/1502.00068v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/83/b2/83b23f4e0771e68f2e112cdcc0a89331dc67997a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1502.00068v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Multi-Dueling Bandits and Their Application to Online Ranker Evaluation [article]

Brian Brost and Yevgeny Seldin and Ingemar J. Cox and Christina Lioma
<span title="2016-08-22">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We evaluate our algorithm on synthetic data and several standard large-scale online ranker evaluation datasets.  ...  Our experimental results show that the algorithm yields orders of magnitude improvement in performance compared to stateof- the-art dueling bandit algorithms.  ...  We also compare the algorithms on four large-scale evaluation datasets summarised in Table 2 1 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1608.06253v1">arXiv:1608.06253v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pvzmbibkd5gtfem4vsafzkfs64">fatcat:pvzmbibkd5gtfem4vsafzkfs64</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191017101307/https://arxiv.org/pdf/1608.06253v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b7/65/b765100b368f2f8d9fa0483e78de31caa3650a96.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1608.06253v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Fast Distributed Bandits for Online Recommendation Systems [article]

Kanak Mahadik, Qingyun Wu, Shuai Li, Amit Sabne
<span title="2020-07-16">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To address the above issues, this paper proposes a novel distributed bandit-based algorithm called DistCLUB.  ...  As a result, these cannot be deployed in practice. The state-of-the-art distributed bandit algorithm - DCCB - relies on a peer-to-peer net-work to share information among distributed workers.  ...  The large-scale at which these web services operate make it necessary to have algorithms that learn fast, in an online fashion.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.08061v1">arXiv:2007.08061v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6zlznh2cjbe5xo76ulcycgh5y4">fatcat:6zlznh2cjbe5xo76ulcycgh5y4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200722024605/https://arxiv.org/pdf/2007.08061v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/dc/ed/dcedde011919b02ab5ace0a4c7aa6ea65f52ea1d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.08061v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Optimizing Ranking Systems Online as Bandits [article]

Chang Li
<span title="2021-10-12">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Bandit is a general online learning framework and can be used in our optimization task.  ...  However, due to the unique features of ranking, there are several challenges in designing bandit algorithms for ranking system optimization.  ...  using dueling bandits for large datasets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.05807v1">arXiv:2110.05807v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mp3fctx6sffhjej7idwc7v33ca">fatcat:mp3fctx6sffhjej7idwc7v33ca</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211015000531/https://arxiv.org/pdf/2110.05807v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/da/ec/daecc30d78c671600fcddf9f04e27696a39c84ee.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.05807v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Automating model search for large scale machine learning

Evan R. Sparks, Ameet Talwalkar, Daniel Haas, Michael J. Franklin, Michael I. Jordan, Tim Kraska
<span title="">2015</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eitdfnn7k5fohgz7jhhim3f4bm" style="color: black;">Proceedings of the Sixth ACM Symposium on Cloud Computing - SoCC &#39;15</a> </i> &nbsp;
by large-scale datasets.  ...  tuning techniques, bandit resource allocation via runtime algorithm introspection, and physical optimization via batching and optimal resource allocation.  ...  ideas about derivative-free optimization and feedback, and Shivaram Venkataraman, Peter Bailis, Alan Fekete, Dan Crankshaw, Sanjay Krishnan, Xinghao Pan, Kevin Jamieson, and our Shepherd, Siddhartha Sen, for  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2806777.2806945">doi:10.1145/2806777.2806945</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cloud/SparksTHFJK15.html">dblp:conf/cloud/SparksTHFJK15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/y4mzheh2ejf5fmimcauahaif5i">fatcat:y4mzheh2ejf5fmimcauahaif5i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808185823/http://web.cs.ucla.edu/~ameet/tupaq_socc.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/83/ec/83ec245d470a3b75c0861acd5e67db5216e8e049.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2806777.2806945"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

A Novel Approach to Address External Validity Issues in Fault Prediction Using Bandit Algorithms

Teruki HAYAKAWA, Masateru TSUNODA, Koji TODA, Keitaro NAKASAI, Amjed TAHIR, Kwabena Ebo BENNIN, Akito MONDEN, Kenichi MATSUMOTO
<span title="2021-02-01">2021</span> <i title="Institute of Electronics, Information and Communications Engineers (IEICE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xosmgvetnbf4zpplikelekmdqe" style="color: black;">IEICE transactions on information and systems</a> </i> &nbsp;
In this work, we propose the use of bandit algorithms in cases where the accuracy of the models are inconsistent across multiple datasets.  ...  Our results showed that bandit algorithms can provide promising outcomes when used in fault prediction. key words: defect prediction, multi-armed bandit, diversity of datasets, dynamic model selection,  ...  Acknowledgments This research was partially supported by the Japan Society for the Promotion of Science (JSPS) [Grants-in-Aid for Scientific Research (C) (No. 20K11749)].  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1587/transinf.2020edl8098">doi:10.1587/transinf.2020edl8098</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/55lig2j3xfbr3lszqrx3bh6udq">fatcat:55lig2j3xfbr3lszqrx3bh6udq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210209191949/https://www.jstage.jst.go.jp/article/transinf/E104.D/2/E104.D_2020EDL8098/_pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f6/e2/f6e24b85bf6e4d2025a0abacc11089b8a2317df1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1587/transinf.2020edl8098"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Balanced Linear Contextual Bandits

Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, Guido Imbens
<span title="2019-07-17">2019</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wtjcymhabjantmdtuptkk62mlq" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;
We demonstrate the strong practical advantage of balanced contextual bandits on a large number of supervised learning datasets and on a synthetic example that simulates model misspecification and prejudice  ...  We provide the first regret bound analyses for linear contextual bandits with balancing and show that our algorithms match the state of the art theoretical guarantees.  ...  the evaluation on classification datasets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v33i01.33013445">doi:10.1609/aaai.v33i01.33013445</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cvzn2dxls5akzgdweu57qy6co4">fatcat:cvzn2dxls5akzgdweu57qy6co4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200306093033/https://aaai.org/ojs/index.php/AAAI/article/download/4221/4099" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d9/cf/d9cf55dbd5a3b2cb3f343ee292ba118049b5505b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v33i01.33013445"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Balanced Linear Contextual Bandits [article]

Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, Guido Imbens
<span title="2018-12-15">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We demonstrate the strong practical advantage of balanced contextual bandits on a large number of supervised learning datasets and on a synthetic example that simulates model misspecification and prejudice  ...  We provide the first regret bound analyses for linear contextual bandits with balancing and show that our algorithms match the state of the art theoretical guarantees.  ...  the evaluation on classification datasets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.06227v1">arXiv:1812.06227v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fjvhmzl3kzb3zfpehdz65aipzu">fatcat:fjvhmzl3kzb3zfpehdz65aipzu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200929144954/https://arxiv.org/pdf/1812.06227v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d3/68/d3687c855977095204baa969335ff4177613bbea.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.06227v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations [article]

Yu Song, Jianxun Lian, Shuai Sun, Hong Huang, Yu Li, Hai Jin, Xing Xie
<span title="2021-10-19">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Contextual bandit (CB) algorithms strive to make a good trade-off between exploration and exploitation so that users' potential interests have chances to expose.  ...  We further propose a progressive hierarchical CB (pHCB) algorithm, which progressively extends visible nodes which reach a confidence level for exploration, to avoid misleading actions on upper-level nodes  ...  Our goal is to propose a generic algorithm that can empower different bandit models to be more effective on large-scale item set exploration.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.09905v1">arXiv:2110.09905v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/af2jsd5r2nhvtc7hmwbdbxwbim">fatcat:af2jsd5r2nhvtc7hmwbdbxwbim</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211021132923/https://arxiv.org/pdf/2110.09905v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ab/8a/ab8a8fe58e1398d64a2a0c3f35aea473c01b0871.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.09905v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Privacy-Preserving Multi-Party Contextual Bandits [article]

Awni Hannun, Brian Knott, Shubho Sengupta, Laurens van der Maaten
<span title="2020-02-13">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Contextual bandits are online learners that, given an input, select an arm and receive a reward for that arm.  ...  This paper develops a privacy-preserving multi-party contextual bandit for this learning setting by combining secure multi-party computation with a differentially private mechanism based on epsilon-greedy  ...  Acknowledgements The authors thank Mark Tygert, Ilya Mironov and Xing Zhou for helpful discussions and comments on early drafts of this paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.05299v3">arXiv:1910.05299v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4yt2qxezifgo3ofqylodcpa6cy">fatcat:4yt2qxezifgo3ofqylodcpa6cy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321124545/https://arxiv.org/pdf/1910.05299v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.05299v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Meta-Learning for Contextual Bandit Exploration [article]

Amr Sharaf, Hal Daumé III
<span title="2019-01-23">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We describe MELEE, a meta-learning algorithm for learning a good exploration policy in the interactive contextual bandit setting.  ...  We compare MELEE to seven strong baseline contextual bandit algorithms on a set of three hundred real-world datasets, on which it outperforms alternatives in most settings, especially when differences  ...  as if it were a contextual bandit dataset.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.08159v1">arXiv:1901.08159v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xly3sqkvsrex3ms6rq5i4tleyu">fatcat:xly3sqkvsrex3ms6rq5i4tleyu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200910185428/https://arxiv.org/pdf/1901.08159v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/30/ae/30ae05d17cc6946ca972ea56eb5b02dc2a401826.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.08159v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Cascading Bandits for Large-Scale Recommendation Problems [article]

Shi Zong, Hao Ni, Kenny Sung, Nan Rosemary Ke, Zheng Wen, and Branislav Kveton
<span title="2016-06-30">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend K most attractive items from a large set of L candidate items.  ...  We propose two algorithms for solving this problem, which are based on the idea of linear generalization.  ...  [24] , which proposes computationally and sample efficient algorithms for large-scale stochastic combinatorial semi-bandits.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1603.05359v2">arXiv:1603.05359v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nbu2h56i5rb4dniquheqtpcjzq">fatcat:nbu2h56i5rb4dniquheqtpcjzq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191014033613/https://arxiv.org/pdf/1603.05359v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/59/75/5975d8a0e79e4b456005610a8bc2432f933f7429.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1603.05359v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Bayesian Linear Bandits for Large-Scale Recommender Systems [article]

Saeed Ghoorchian, Setareh Maghsudi
<span title="2022-02-07">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
For numerical evaluation, we use our algorithm to build a recommender system and apply it to real-world datasets.  ...  We develop a decision-making policy for a linear bandit problem with high-dimensional context vectors and several arms.  ...  As expected, our algorithm surpasses CBRAP in terms of the average runtime and cumulative reward for the MovieLens dataset, it corresponds to a large-scale scenario with many items and high-dimensional  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.03167v1">arXiv:2202.03167v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/owzxaod45bbh3fvutxlghmkgse">fatcat:owzxaod45bbh3fvutxlghmkgse</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220209004021/https://arxiv.org/pdf/2202.03167v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4a/95/4a9545fe2672a3fc0159c356010fb4bcfc6938f3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.03167v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
&laquo; Previous Showing results 1 &mdash; 15 out of 1,982 results