This map shows the geographic impact of Martha White'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 Martha White with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martha White more than expected).
This network shows the impact of papers produced by Martha White. 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 Martha White. The network helps show where Martha White may publish in the future.
Co-authorship network of co-authors of Martha White
This figure shows the co-authorship network connecting the top 25 collaborators of Martha White.
A scholar is included among the top collaborators of Martha White 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 Martha White. Martha White is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Liu, Vincent, et al.. (2020). Training Recurrent Neural Networks Online by Learning Explicit State Variables. International Conference on Learning Representations.3 indexed citations
5.
Zaheer, Muhammad Zaigham, et al.. (2020). Selective Dyna-style Planning Under Limited Model Capacity. International Conference on Machine Learning. 1. 1–10.
Le, Lei, Andrew Patterson, & Martha White. (2018). Supervised autoencoders: Improving generalization performance with unsupervised regularizers. Neural Information Processing Systems. 31. 107–117.95 indexed citations
8.
Sherstan, Craig, et al.. (2018). Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return. Uncertainty in Artificial Intelligence. 63–72.3 indexed citations
9.
White, Martha, et al.. (2017). Adapting Kernel Representations Online Using Submodular Maximization. International Conference on Machine Learning. 3037–3046.3 indexed citations
Jain, Shantanu, Martha White, & Predrag Radivojac. (2016). Estimating the class prior and posterior from noisy positives and unlabeled data. Neural Information Processing Systems. 29. 2685–2693.11 indexed citations
14.
White, Martha. (2014). Integrating Representation Learning and Temporal Difference Learning: A Matrix Factorization Approach. National Conference on Artificial Intelligence.1 indexed citations
15.
Degris, Thomas, Martha White, & Richard S. Sutton. (2012). Linear Off-Policy Actor-Critic.. International Conference on Machine Learning.9 indexed citations
White, Martha & Dale Schuurmans. (2012). Generalized Optimal Reverse Prediction. International Conference on Artificial Intelligence and Statistics. 1305–1313.3 indexed citations
18.
White, Martha & Adam White. (2010). Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains. Neural Information Processing Systems. 23. 2433–2441.9 indexed citations
19.
Yang, Min, Linli Xu, Martha White, Dale Schuurmans, & Yaoliang Yu. (2010). Relaxed Clipping: A Global Training Method for Robust Regression and Classification. Neural Information Processing Systems. 23. 2532–2540.20 indexed citations
20.
White, Martha & Michael Bowling. (2009). Learning a value analysis tool for agent evaluation. International Joint Conference on Artificial Intelligence. 1976–1981.11 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.