Ian Osband

4.8k total citations · 1 hit paper
20 papers, 1.4k citations indexed

About

Ian Osband is a scholar working on Artificial Intelligence, Management Science and Operations Research and Economics and Econometrics. According to data from OpenAlex, Ian Osband has authored 20 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 9 papers in Management Science and Operations Research and 2 papers in Economics and Econometrics. Recurrent topics in Ian Osband's work include Reinforcement Learning in Robotics (13 papers), Advanced Bandit Algorithms Research (9 papers) and Machine Learning and Algorithms (4 papers). Ian Osband is often cited by papers focused on Reinforcement Learning in Robotics (13 papers), Advanced Bandit Algorithms Research (9 papers) and Machine Learning and Algorithms (4 papers). Ian Osband collaborates with scholars based in United States, United Kingdom and France. Ian Osband's co-authors include Benjamin Van Roy, Zheng Wen, Daniel Russo, Abbas Kazerouni, Olivier Pietquin, Bilal Piot, Tom Schaul, Todd Hester, Marc Lanctot and Joel Z. Leibo and has published in prestigious journals such as Journal of Machine Learning Research, now publishers, Inc. eBooks and arXiv (Cornell University).

In The Last Decade

Ian Osband

19 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Q-learning From Demonstrations

2018 485 citations Todd Hester, Olivier Pietquin et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers

Countries citing papers authored by Ian Osband

Since Specialization

Citations

This map shows the geographic impact of Ian Osband's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ian Osband with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ian Osband more than expected).

Fields of papers citing papers by Ian Osband

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ian Osband. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ian Osband. The network helps show where Ian Osband may publish in the future.

Co-authorship network of co-authors of Ian Osband

This figure shows the co-authorship network connecting the top 25 collaborators of Ian Osband. A scholar is included among the top collaborators of Ian Osband based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ian Osband. Ian Osband is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Reinforcement Learning, Bit by Bit 2023 ·Xiuyuan Lu, Benjamin Van Roy, (unknown), (unknown), Ian Osband, Zheng Wen	7
2	Reinforcement Learning, Bit by Bit 2023 ·Xiuyuan Lu, Benjamin Van Roy, (unknown), (unknown), Ian Osband, Zheng Wen	2
3	Matrix games with bandit feedback 2021 ·Uncertainty in Artificial Intelligence ·Brendan O’Donoghue, Tor Lattimore, Ian Osband	1
4	Hypermodels for Exploration 2020 ·arXiv (Cornell University) ·(unknown), Xiuyuan Lu, (unknown), Ian Osband, Zheng Wen, Benjamin Van Roy	1
5	Making Sense of Reinforcement Learning and Probabilistic Inference 2020 ·arXiv (Cornell University) ·Brendan O’Donoghue, Ian Osband, Catalin Ionescu	4
6	Deep Exploration via Randomized Value Functions 2019 ·Journal of Machine Learning Research ·Ian Osband, Benjamin Van Roy, Daniel Russo, Zheng Wen	47
7	Randomized prior functions for deep reinforcement learning 2018 ·Neural Information Processing Systems ·Ian Osband, John Aslanides, Albin Cassirer	27
8	Noisy Networks For Exploration 2018 ·arXiv (Cornell University) ·Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alexander Graves, (unknown), Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg	115
9	The Uncertainty Bellman Equation and Exploration. 2018 ·International Conference on Machine Learning ·Brendan O’Donoghue, Ian Osband, Rémi Munos, Volodymyr Mnih	13
10	Scalable Coordinated Exploration in Concurrent Reinforcement Learning 2018 ·arXiv (Cornell University) ·(unknown), Ian Osband, Benjamin Van Roy	3
11	A Tutorial on Thompson Sampling 2018 ·Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen	260
12	A Tutorial on Thompson Sampling 2018 ·now publishers, Inc. eBooks ·Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen	181
13	Deep Q-learning From Demonstrations breakdown → 2018 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Todd Hester, (unknown), Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, (unknown), Ian Osband, Gabriel Dulac-Arnold, John Agapiou, Joel Z. Leibo, Audrūnas Gruslys	485
14	Learning from Demonstrations for Real World Reinforcement Learning 2017 ·arXiv (Cornell University) ·Todd Hester, (unknown), Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, (unknown), Gabriel Dulac-Arnold, Ian Osband, John Agapiou, Joel Z. Leibo, Audrūnas Gruslys	43
15	Deep Q-learning from Demonstrations 2017 ·arXiv (Cornell University) ·Todd Hester, (unknown), Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, (unknown), Gabriel Dulac-Arnold, Ian Osband, John Agapiou, Joel Z. Leibo, Audrūnas Gruslys	123
16	Model-based Reinforcement Learning and the Eluder Dimension 2014 ·arXiv (Cornell University) ·Ian Osband, Benjamin Van Roy	7
17	Near-optimal Reinforcement Learning in Factored MDPs 2014 ·arXiv (Cornell University) ·Ian Osband, Benjamin Van Roy	20
18	Near-optimal Regret Bounds for Reinforcement Learning in Factored MDPs. 2014 ·Ian Osband, Benjamin Van Roy	1
19	(More) Efficient Reinforcement Learning via Posterior Sampling 2013 ·arXiv (Cornell University) ·Ian Osband, (unknown), Benjamin Van Roy	77
20	Deep Learning for Time Series Modeling CS 229 Final Project Report 2012 ·(unknown), Ian Osband, (unknown)	14

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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