Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Information theoretic MPC for model-based reinforcement learning
2017262 citationsNolan Wagener, Byron Boots et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Byron Boots'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 Byron Boots with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Byron Boots more than expected).
This network shows the impact of papers produced by Byron Boots. 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 Byron Boots. The network helps show where Byron Boots may publish in the future.
Co-authorship network of co-authors of Byron Boots
This figure shows the co-authorship network connecting the top 25 collaborators of Byron Boots.
A scholar is included among the top collaborators of Byron Boots 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 Byron Boots. Byron Boots is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Miller, Jacob, et al.. (2021). Quantum Tensor Networks, Stochastic Processes, and Weighted Automata. International Conference on Artificial Intelligence and Statistics. 2080–2088.1 indexed citations
9.
Lambert, Alexander, et al.. (2020). Stein Variational Model Predictive Control. 1278–1297.2 indexed citations
Fox, Dieter, et al.. (2020). Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems. 630–639.3 indexed citations
12.
Li, Anqi, Harish Ravichandar, Mustafa Mukadam, et al.. (2019). Learning Reactive Motion Policies in Multiple Task Spaces from Human Demonstrations.. 1457–1468.6 indexed citations
Downey, Carlton, et al.. (2018). Learning and Inference in Hilbert Space with Quantum Graphical Models. arXiv (Cornell University). 31. 10338–10347.1 indexed citations
15.
Pan, Yunpeng, Ching-An Cheng, Kamil Saigol, et al.. (2017). Imitation Learning for Agile Autonomous Driving. arXiv (Cornell University).1 indexed citations
16.
Boots, Byron, et al.. (2016). The Nonparametric Kernel Bayes Smoother. International Conference on Artificial Intelligence and Statistics. 547–555.6 indexed citations
17.
Sun, Wen, Arun Venkatraman, Byron Boots, & J. Andrew Bagnell. (2016). Learning to filter with predictive state inference machines. International Conference on Machine Learning. 1197–1205.8 indexed citations
18.
Cheng, Ching-An & Byron Boots. (2016). Incremental Variational Sparse Gaussian Process Regression. Neural Information Processing Systems. 29. 4403–4411.12 indexed citations
Majercik, Stephen M. & Byron Boots. (2005). DC-SSAT: a divide-and-conquer approach to solving stochastic satisfiability problems efficiently. Bowdoin - Digital Commons (Bowdoin College). 416–422.11 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.