Travis Dick

489 citations
21 papers · 188 · h-index 9

Impact in

Papers in

    • Machine Learning and Algorithms 6
    • Privacy-Preserving Technologies in Data 5
    • Cryptography and Data Security 2
    • Advanced Clustering Algorithms Research 2
    • Machine Learning and Data Classification 2
    • Bayesian Methods and Mixture Models 2
    • Constraint Satisfaction and Optimization 2

Travis Dick

20 papers receiving 177 citations

Peers

Travis Dick
Comparison fields: 5 of 55
  • Human-Computer Interaction 19
  • Artificial Intelligence 91
  • Computer Science Applications 14
  • Management Science and Operations Research 24
  • Health Informatics 2
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Countries citing papers authored by Travis Dick

Since Specialization
Citations

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

Fields of papers citing papers by Travis Dick

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201329
2
Differentially Private Clustering in High-Dimensional Euclidean Spaces
201724
3 201323
4 201820
5 201917
6
Learning to Branch
201814
7 202311
8 20239
9
Differentially Private Covariance Estimation
20198
10 20138
11
Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images
20207
12
Envy-Free Classification
20193
13 20153
14 20183
15 20232
16
Data-Driven Clustering via Parameterized Lloyd's Families
20182
17 20172
18
Learning piecewise Lipschitz functions in changing environments
20201
19
Learning to Link
20201
20
Learning piecewise Lipschitz functions in changing environments
20201

About Travis Dick

Travis Dick is a scholar working on Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Cognitive Neuroscience and Management Science and Operations Research, having authored 21 papers that have together received 188 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (6 papers), Privacy-Preserving Technologies in Data (5 papers), Advanced Bandit Algorithms Research (3 papers), Cryptography and Data Security (2 papers), Constraint Satisfaction and Optimization (2 papers), Advanced Clustering Algorithms Research (2 papers), Machine Learning and Data Classification (2 papers) and Bayesian Methods and Mixture Models (2 papers). The work is most often cited by research in Human-Computer Interaction (19 citations), Artificial Intelligence (91 citations), Computer Science Applications (14 citations), Management Science and Operations Research (24 citations) and Health Informatics (2 citations). Travis Dick has collaborated with scholars based in United States, Canada and India. Frequent co-authors include Maria-Florina Balcan, Patrick M. Pilarski, Richard S. Sutton, Martin Jägersand, Azad Shademan, Natalie Rudolph, Jianxing Chen, Yingyu Liang, Camilo Perez Quintero and Hongyang Zhang. Their work appears in journals such as Journal of the ACM, American Journal of Health-System Pharmacy, Journal of Machine Learning Research, Proceedings of the National Academy of Sciences and University of Alberta Library.

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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