Jacob Abernethy

2.6k citations
64 papers · 1.0k · h-index 17

Impact in

Papers in

Jacob Abernethy

63 papers receiving 946 citations

Peers

Jacob Abernethy
Comparison fields: 5 of 84
  • Management Science and Operations Research 599
  • Artificial Intelligence 580
  • Marketing 123
  • Computer Networks and Communications 225
  • General Decision Sciences 13
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Countries citing papers authored by Jacob Abernethy

Since Specialization
Citations

This map shows the geographic impact of Jacob Abernethy'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 Jacob Abernethy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jacob Abernethy more than expected).

Fields of papers citing papers by Jacob Abernethy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jacob Abernethy. 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 Jacob Abernethy. The network helps show where Jacob Abernethy may publish in the future.

Co-authors

The 25 scholars most cited alongside Jacob Abernethy, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jacob Abernethy Line = papers co-authored together Jacob Abernethy links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 64 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019113
2
Competing in the dark: An efficient algorithm for bandit linear optimization
2008110
3 200861
4
Optimal Strategies and Minimax Lower Bounds for Online Convex Games
200852
5 201049
6 201347
7
A Stochastic View of Optimal Regret through Minimax Duality
200945
8 200844
9 201137
10
Blackwell Approachability and No-Regret Learning are Equivalent
201135
11 201229
12 201126
13
WITCH: A NEW APPROACH TO WEB SPAM DETECTION
200825
14 200723
15 200922
16
A Characterization of Scoring Rules for Linear Properties
201222
17
Optimal Stragies and Minimax Lower Bounds for Online Convex Games.
200817
18
Beating the adaptive bandit with high probability
200916
19
When Random Play is Optimal Against an Adversary.
200815
20
How to Train Your DRAGAN
201713

About Jacob Abernethy

Jacob Abernethy is a scholar working on Management Science and Operations Research, Artificial Intelligence, Computer Networks and Communications, Economics and Econometrics and Marketing, having authored 64 papers that have together received 1.0k indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (45 papers), Machine Learning and Algorithms (19 papers), Auction Theory and Applications (16 papers), Optimization and Search Problems (13 papers), Reinforcement Learning in Robotics (10 papers), Sports Analytics and Performance (8 papers), Stochastic Gradient Optimization Techniques (8 papers) and Consumer Market Behavior and Pricing (8 papers). The work is most often cited by research in Management Science and Operations Research (599 citations), Artificial Intelligence (580 citations), Marketing (123 citations), Computer Networks and Communications (225 citations) and General Decision Sciences (13 citations). Jacob Abernethy has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Alexander Rakhlin, Elad Hazan, Peter L. Bartlett, Eric M. Schwartz, Kanishka Misra, Olivier Chapelle, Carlos Castillo, Jennifer Wortman Vaughan, Yiling Chen and Rafael Frongillo. Their work appears in journals such as IEEE Transactions on Information Theory, IEEE Transactions on Knowledge and Data Engineering, Machine Learning, Journal of Machine Learning Research and Mathematical Programming.

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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