Travis Dick

489 total citations
21 papers, 188 citations indexed

About

Travis Dick is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Travis Dick has authored 21 papers receiving a total of 188 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 3 papers in Computer Networks and Communications and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Travis Dick's work include Machine Learning and Algorithms (6 papers), Privacy-Preserving Technologies in Data (5 papers) and Advanced Bandit Algorithms Research (3 papers). Travis Dick is often cited by papers focused on Machine Learning and Algorithms (6 papers), Privacy-Preserving Technologies in Data (5 papers) and Advanced Bandit Algorithms Research (3 papers). Travis Dick collaborates with scholars based in United States, Canada and India. Travis Dick's co-authors include Maria-Florina Balcan, Richard S. Sutton, Patrick M. Pilarski, Azad Shademan, Martin Jägersand, Natalie Rudolph, Jianxing Chen, Camilo Perez Quintero, Yingyu Liang and Wenlong Mou and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the ACM and Journal of Machine Learning Research.

In The Last Decade

Travis Dick

20 papers receiving 177 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Travis Dick United States	9	91	29	25	24	24	21	188
Norah Alnaim Saudi Arabia	11	89 1.0×	21 0.7×	62 2.5×	18 0.8×	12 0.5×	22	283
David Ha United States	6	121 1.3×	18 0.6×	61 2.4×	42 1.8×	9 0.4×	17	234
Sebastián Basterrech Czechia	8	101 1.1×	11 0.4×	16 0.6×	10 0.4×	9 0.4×	30	227
Berat A. Erol United States	11	84 0.9×	9 0.3×	76 3.0×	35 1.5×	8 0.3×	17	258
Pratik Kanani India	8	69 0.8×	23 0.8×	66 2.6×	8 0.3×	6 0.3×	42	268
Sylvain Bruni United States	6	59 0.6×	7 0.2×	33 1.3×	21 0.9×	12 0.5×	18	221
Gilles Coppin France	7	46 0.5×	6 0.2×	28 1.1×	20 0.8×	19 0.8×	29	181
Daniel Hládek Slovakia	9	194 2.1×	9 0.3×	40 1.6×	51 2.1×	17 0.7×	50	295
Dylan Hadfield-Menell United States	9	167 1.8×	17 0.6×	78 3.1×	94 3.9×	27 1.1×	23	297

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-authorship network of co-authors of Travis Dick

This figure shows the co-authorship network connecting the top 25 collaborators of Travis Dick. A scholar is included among the top collaborators of Travis Dick 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 Travis Dick. Travis Dick 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

Balcan, Maria-Florina, et al.. (2024). How Much Data Is Sufficient to Learn High-Performing Algorithms?. Journal of the ACM. 71(5). 1–58.

Carey, CJ, Travis Dick, Alessandro Epasto, et al.. (2023). Measuring Re-identification Risk. Proceedings of the ACM on Management of Data. 1(2). 1–26. 9 indexed citations

Balcan, Maria-Florina, et al.. (2023). Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization. Journal of the ACM. 71(2). 1–73. 2 indexed citations

Dick, Travis, Cynthia Dwork, Michael Kearns, et al.. (2023). Confidence-ranked reconstruction of census microdata from published statistics. Proceedings of the National Academy of Sciences. 120(8). e2218605120–e2218605120. 11 indexed citations

Balcan, Maria-Florina, et al.. (2020). Learning piecewise Lipschitz functions in changing environments. International Conference on Artificial Intelligence and Statistics. 3567–3577. 1 indexed citations

Balcan, Maria-Florina, et al.. (2020). Learning piecewise Lipschitz functions in changing environments. 108. 1 indexed citations

Balcan, Maria-Florina, Travis Dick, & Manuel Lang. (2020). Learning to Link. arXiv (Cornell University). 1 indexed citations

Blum, Avrim, et al.. (2020). Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images. Journal of Machine Learning Research. 21(211). 1–21. 7 indexed citations

Balcan, Maria-Florina, Travis Dick, Ritesh Noothigattu, & Ariel D. Procaccia. (2019). Envy-Free Classification. arXiv (Cornell University). 32. 1240–1250. 3 indexed citations

10.

Amin, Kareem, et al.. (2019). Differentially Private Covariance Estimation. Neural Information Processing Systems. 32. 14190–14199. 8 indexed citations

11.

Rudolph, Natalie, Jianxing Chen, & Travis Dick. (2019). Understanding the temperature field in fused filament fabrication for enhanced mechanical part performance. AIP conference proceedings. 2055. 140003–140003. 17 indexed citations

12.

Balcan, Maria-Florina, et al.. (2018). Learning to Branch. International Conference on Machine Learning. 344–353. 14 indexed citations

13.

Balcan, Maria-Florina, Travis Dick, & Colin White. (2018). Data-Driven Clustering via Parameterized Lloyd's Families. arXiv (Cornell University). 31. 10641–10651. 2 indexed citations

14.

Balcan, Maria-Florina, et al.. (2018). Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization. 603–614. 20 indexed citations

15.

Dick, Travis, et al.. (2018). Fundamentals of leading, tools for managing, and strategies for sustaining change. American Journal of Health-System Pharmacy. 75(19). 1450–1455. 3 indexed citations

16.

Balcan, Maria-Florina, Travis Dick, Yingyu Liang, Wenlong Mou, & Hongyang Zhang. (2017). Differentially Private Clustering in High-Dimensional Euclidean Spaces. International Conference on Machine Learning. 322–331. 24 indexed citations

17.

Balcan, Maria Florina, Travis Dick, & Yishay Mansour. (2017). Label Efficient Learning by Exploiting Multi-Class Output Codes. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 2 indexed citations

18.

Dick, Travis. (2015). Policy Gradient Reinforcement Learning Without Regret. University of Alberta Library. 3 indexed citations

19.

Dick, Travis, et al.. (2013). Realtime Registration-Based Tracking via Approximate Nearest Neighbour Search. 8 indexed citations

20.

Pilarski, Patrick M., Travis Dick, & Richard S. Sutton. (2013). Real-time prediction learning for the simultaneous actuation of multiple prosthetic joints. PubMed. 2013. 1–8. 23 indexed citations

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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