Timothy Mann

1.7k citations
33 papers · 202 · h-index 8

Impact in

    • Reinforcement Learning in Robotics
    • Adversarial Robustness in Machine Learning
    • Anomaly Detection Techniques and Applications
    • Machine Learning and Algorithms
    • Evolutionary Algorithms and Applications
    • AI-based Problem Solving and Planning
    • Advanced Bandit Algorithms Research

Papers in

Timothy Mann

28 papers receiving 191 citations

Peers

Timothy Mann
Comparison fields: 5 of 52
  • Artificial Intelligence 166
  • Management Science and Operations Research 31
  • Hardware and Architecture 11
  • Computer Vision and Pattern Recognition 32
  • Computational Theory and Mathematics 24
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Citations per field
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Citations per year

Countries citing papers authored by Timothy Mann

Since Specialization
Citations

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

Fields of papers citing papers by Timothy Mann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Timothy Mann, 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 Timothy Mann Line = papers co-authored together Timothy Mann links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201964
2
Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations
201420
3
Directed Exploration in Reinforcement Learning with Transferred Knowledge
201214
4
How hard is my MDP?" The distribution-norm to the rescue"
201411
5 20159
6
Adaptive Skills Adaptive Partitions (ASAP)
20168
7
Time-regularized interrupting options
20147
8 20187
9
Off-policy Model-based Learning under Unknown Factored Dynamics
20156
10
Self-supervised Adversarial Robustness for the Low-label, High-data Regime
20216
11 20185
12 20115
13
A Bayesian Approach to Robust Reinforcement Learning
20194
14 20124
15 20104
16 20214
17
Learning from Delayed Outcomes with Intermediate Observations
20183
18
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
20193
19 20093
20 20193

About Timothy Mann

Timothy Mann is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Cognitive Neuroscience and Computer Vision and Pattern Recognition, having authored 33 papers that have together received 202 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (15 papers), Machine Learning and Algorithms (8 papers), Adversarial Robustness in Machine Learning (7 papers), Advanced Bandit Algorithms Research (4 papers), Action Observation and Synchronization (3 papers), Evolutionary Algorithms and Applications (3 papers), AI-based Problem Solving and Planning (3 papers) and Optimization and Search Problems (2 papers). The work is most often cited by research in Artificial Intelligence (166 citations), Management Science and Operations Research (31 citations), Hardware and Architecture (11 citations), Computer Vision and Pattern Recognition (32 citations) and Computational Theory and Mathematics (24 citations). Timothy Mann has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Shie Mannor, Yoonsuck Choe, Sven Gowal, Daniel J. Mankowitz, Pushmeet Kohli, Robert Stanforth, Krishnamurthy Dvijotham, Jonathan Uesato, Rudy Bunel and Relja Arandjelović. Their work appears in journals such as Behavioral and Brain Sciences, Journal of Artificial Intelligence Research, Journal of Pharmacy Practice and Research, BMC Neuroscience and ICGA Journal.

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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