Most cited papers in Advanced Bandit Algorithms Research
What are the most cited papers in Advanced Bandit Algorithms Research?
The most cited paper in Advanced Bandit Algorithms Research is "Adam: A Method for Stochastic Optimization" (Diederik P. Kingma, Jimmy Lei Ba, 2014), with 84,625 citations. Next come "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting" (20,713) and "No free lunch theorems for optimization" (14,318).
Showing 50 of 99 papers. Every paper with more than 664 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Adam: A Method for Stochastic Optimization Diederik P. Kingma, Jimmy Lei Ba · arXiv (Cornell University) | 22 Dec 2014 | 84,625 |
| 2 | A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting Yoav Freund, Robert E. Schapire · Journal of Computer and System Sciences | Aug 1997 | 20,713 |
| 3 | No free lunch theorems for optimization David H. Wolpert, William G. Macready · IEEE Transactions on Evolutionary Computation | Apr 1997 | 14,318 |
| 4 | Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper) Esper, Khalil, Spieck, Jan, Sixdenier, Pierre-Louis et al. · arXiv (Cornell University) | 20 Jul 2017 | 11,361 |
| 5 | Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions Gediminas Adomavičius, Alexander Tuzhilin · IEEE Transactions on Knowledge and Data Engineering | 25 Apr 2005 | 10,402 |
| 6 | Item-based collaborative filtering recommendation algorithms Badrul M. Sarwar, George Karypis, Joseph A. Konstan et al. | Apr 2001 | 9,115 |
| 7 | Adaptive Subgradient Methods for Online Learning and Stochastic Optimization John C. Duchi, Elad Hazan, Yoram Singer | 2010 | 8,604 |
| 8 | Taking the Human Out of the Loop: A Review of Bayesian Optimization Bobak Shahriari, Kevin Swersky, Ziyu Wang et al. · Proceedings of the IEEE | 10 Dec 2015 | 6,252 |
| 9 | Finite-time Analysis of the Multiarmed Bandit Problem Peter L. Auer, Nicolò Cesa‐Bianchi, Paul Fischer · Machine Learning | May 2002 | 5,993 |
| 10 | Evaluating collaborative filtering recommender systems Jonathan L. Herlocker, Joseph A. Konstan, Loren Terveen et al. · ACM Transactions on Information Systems | 2004 | 5,828 |
| 11 | Large-Scale Machine Learning with Stochastic Gradient Descent Léon Bottou | 2010 | 5,757 |
| 12 | Factorization meets the neighborhood Yehuda Koren | 24 Aug 2008 | 3,964 |
| 13 | Wide & Deep Learning for Recommender Systems Heng-Tze Cheng, Levent Koç, Jeremiah Harmsen et al. | 15 Sep 2016 | 3,424 |
| 14 | Collaborative Filtering for Implicit Feedback Datasets Yifan Hu, Yehuda Koren, Chris T. Volinsky | Dec 2008 | 3,259 |
| 15 | Bandit Based Monte-Carlo Planning Levente Kocsis, Csaba Szepesvári · Lecture notes in computer science | 2006 | 2,928 |
| 16 | Factorizing personalized Markov chains for next-basket recommendation Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme | 26 Apr 2010 | 2,765 |
| 17 | A contextual-bandit approach to personalized news article recommendation Lihong Li, Wei Chu, John C. Langford et al. | 26 Apr 2010 | 2,613 |
| 18 | Asymptotically efficient adaptive allocation rules Tze-Leung Lai, Herbert E. Robbins · Advances in Applied Mathematics | Mar 1985 | 2,525 |
| 19 | Boosting the margin: a new explanation for the effectiveness of voting methods Peter L. Bartlett, Yoav Freund, Wee Sun Lee et al. · The Annals of Statistics | Oct 1998 | 2,345 |
| 20 | The Nonstochastic Multiarmed Bandit Problem Peter L. Auer, Nicolò Cesa‐Bianchi, Yoav Freund et al. · SIAM Journal on Computing | 2002 | 2,272 |
| 21 | A desicion-theoretic generalization of on-line learning and an application to boosting Yoav Freund, Robert E. Schapire · Lecture notes in computer science | 1995 | 2,245 |
| 22 | Probabilistic machine learning and artificial intelligence Zoubin Ghahramani · Nature | 26 May 2015 | 2,047 |
| 23 | The Weighted Majority Algorithm Nicholas Littlestone, Manfred K. Warmuth · Information and Computation | Feb 1994 | 2,040 |
| 24 | The information bottleneck method Tishby, Naftali, Fernando C. N. Pereira, William Bialek · arXiv (Cornell University) | 24 Apr 2000 | 1,861 |
| 25 | Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments Amir-Hamed Mohsenian-Rad, Alberto Leon‐Garcia · IEEE Transactions on Smart Grid | 10 Aug 2010 | 1,846 |
| 26 | Group sequential methods in the design and analysis of clinical trials Stuart Pocock · Biometrika | Aug 1977 | 1,798 |
| 27 | Methods and metrics for cold-start recommendations Andrew I. Schein, Alexandrin Popescul, Lyle Ungar et al. | 11 Aug 2002 | 1,777 |
| 28 | A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions Eric R. Schulz, Maarten Speekenbrink, Andreas Krause · Journal of Mathematical Psychology | 26 May 2018 | 1,753 |
| 29 | Deterministic Policy Gradient Algorithms David Lawrence Silver, Guy Lever, Nicolas Heess et al. · HAL (Le Centre pour la Communication Scientifique Directe) | 2014 | 1,735 |
| 30 | Online convex programming and generalized infinitesimal gradient ascent Martin Zinkevich | 21 Aug 2003 | 1,691 |
| 31 | Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods Tamara G. Kolda, Robert Michael Lewis, Virginia J. Torczon · SIAM Review | 2003 | 1,661 |
| 32 | Neural Attentive Session-based Recommendation Jing Li, Pengjie Ren, Zhumin Chen et al. | 6 Nov 2017 | 1,541 |
| 33 | Temporal difference learning and TD-Gammon Gerald Tesauro · Communications of the ACM | Mar 1995 | 1,496 |
| 34 | Performance of recommender algorithms on top-n recommendation tasks Paolo Cremonesi, Yehuda Koren, Roberto Turrin | 26 Sep 2010 | 1,447 |
| 35 | Online Passive-Aggressive Algorithms Koby Crammer, Ofer Dekel, Joseph Keshet et al. | 2003 | 1,443 |
| 36 | The Netflix Recommender System Carlos Alberto Gomez-Uribe, Neil T. Hunt · ACM Transactions on Management Information Systems | 28 Dec 2015 | 1,400 |
| 37 | Context‐Aware Recommender Systems Gediminas Adomavičius, Bamshad Mobasher, Francesco Ricci⋆ et al. · AI Magazine | Sep 2011 | 1,370 |
| 38 | Auto-WEKA Chris Thornton, Frank Hutter, Holger H. Hoos et al. | 11 Aug 2013 | 1,362 |
| 39 | Sensor Selection via Convex Optimization Shashank V. Joshi, Stephen Boyd · IEEE Transactions on Signal Processing | 6 Nov 2008 | 1,345 |
| 40 | Evaluating Recommendation Systems Guy Shani, Asela Gunawardana | 5 Oct 2010 | 1,338 |
| 41 | An Introduction to Deep Reinforcement Learning Vincent François-Lavet, Peter B. Henderson, Riashat Islam et al. · Foundations and Trends® in Machine Learning | 20 Dec 2018 | 1,295 |
| 42 | Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems Sébastien Bubeck, Nicolò Cesa-Bianchi · Foundations and Trends® in Machine Learning | 12 Dec 2012 | 1,283 |
| 43 | Online Learning and Online Convex Optimization Shai Shalev‐Shwartz · Foundations and Trends® in Machine Learning | 29 Mar 2012 | 1,258 |
| 44 | Using confidence bounds for exploitation-exploration trade-offs Peter L. Auer | Mar 2003 | 1,248 |
| 45 | Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts Jiaqi Ma, Zhe Zhao, Xinyang Yi et al. | 19 Jul 2018 | 1,233 |
| 46 | Incorporating contextual information in recommender systems using a multidimensional approach Gediminas Adomavičius, Ramesh Sankaranarayanan, Shahana Sen et al. · ACM Transactions on Information Systems | 2005 | 1,194 |
| 47 | Being accurate is not enough Sean M. McNee, John Riedl, Joseph A. Konstan | 21 Apr 2006 | 1,072 |
| 48 | Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization Lisha Li, Kevin Jamieson, Giulia DeSalvo et al. · arXiv (Cornell University) | 21 Mar 2016 | 1,070 |
| 49 | Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding Richard S. Sutton | 27 Nov 1995 | 1,069 |
| 50 | Multiple Object Tracking Using K-Shortest Paths Optimization Jérôme Berclaz, François Fleuret, Engin Türetken et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 3 Feb 2011 | 1,046 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.