Jacob Abernethy

2.6k citations
58 papers · 838 · h-index 16

Impact in

Papers in

Jacob Abernethy

57 papers receiving 775 citations

Peers

Jacob Abernethy
Comparison fields: 5 of 75
  • Management Science and Operations Research 495
  • Artificial Intelligence 472
  • Marketing 107
  • Computer Networks and Communications 192
  • General Decision Sciences 13
Replace Dávid Pál with:
Dávid Pál Canada
Bowen Zhang China
Eilon Solan Israel
Tyler Lu Canada
Brendan Lucier United States
Shaddin Dughmi United States
Hennie Daniels Netherlands
Onno Zoeter Netherlands
Katrina Ligett United States
Piotr Faliszewski Poland
Jacob Abernethy relative to Dávid Pál Canada Dávid Pál's profile →
Citations per field
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Citations per year

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 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Competing in the dark: An efficient algorithm for bandit linear optimization
200899
2 201997
3 200856
4
Optimal Strategies and Minimax Lower Bounds for Online Convex Games
200847
5 201044
6 200843
7
A Stochastic View of Optimal Regret through Minimax Duality
200941
8 201340
9 201134
10
Blackwell Approachability and No-Regret Learning are Equivalent
201126
11 201124
12
A Characterization of Scoring Rules for Linear Properties
201221
13
WITCH: A NEW APPROACH TO WEB SPAM DETECTION
200821
14 201220
15
Optimal Stragies and Minimax Lower Bounds for Online Convex Games.
200816
16
Beating the adaptive bandit with high probability
200915
17
When Random Play is Optimal Against an Adversary.
200813
18
How to Train Your DRAGAN
201712
19 200911
20 201611

About Jacob Abernethy

Jacob Abernethy is a scholar working on Management Science and Operations Research, Artificial Intelligence, Economics and Econometrics, Computer Networks and Communications and Marketing, having authored 58 papers that have together received 838 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (42 papers), Machine Learning and Algorithms (17 papers), Auction Theory and Applications (16 papers), Optimization and Search Problems (10 papers), Reinforcement Learning in Robotics (10 papers), Consumer Market Behavior and Pricing (8 papers), Stochastic Gradient Optimization Techniques (8 papers) and Sports Analytics and Performance (8 papers). The work is most often cited by research in Management Science and Operations Research (495 citations), Artificial Intelligence (472 citations), Marketing (107 citations), Computer Networks and Communications (192 citations) and General Decision Sciences (13 citations). Jacob Abernethy has collaborated with scholars based in United States, Israel and Spain. Frequent co-authors include Alexander Rakhlin, Elad Hazan, Eric M. Schwartz, Kanishka Misra, Peter L. Bartlett, Olivier Chapelle, Carlos Castillo, Rafael Frongillo, Yiling Chen and Jennifer Wortman Vaughan. Their work appears in journals such as Marketing Science, Machine Learning, Mathematical Programming, IEEE Transactions on Information Theory and Journal of Machine Learning Research.

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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