Manfred K. Warmuth
About
In The Last Decade
Manfred K. Warmuth
149 papers receiving 8.1k citations
Hit Papers
Peers
Comparison fields: 5 of 188
- Artificial Intelligence 6.4k
- Management Science and Operations Research 2.0k
- Computational Theory and Mathematics 1.9k
- Computer Networks and Communications 1.7k
- Computer Vision and Pattern Recognition 1.2k
Countries citing papers authored by Manfred K. Warmuth
This map shows the geographic impact of Manfred K. Warmuth'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 Manfred K. Warmuth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manfred K. Warmuth more than expected).
Fields of papers citing papers by Manfred K. Warmuth
This network shows the impact of papers produced by Manfred K. Warmuth. 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 Manfred K. Warmuth. The network helps show where Manfred K. Warmuth may publish in the future.
Co-authorship network of co-authors of Manfred K. Warmuth
This figure shows the co-authorship network connecting the top 25 collaborators of Manfred K. Warmuth. A scholar is included among the top collaborators of Manfred K. Warmuth 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 Manfred K. Warmuth. Manfred K. Warmuth is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Correcting the bias in least squares regression with volume-rescaled sampling | 2 |
| 2 | Subsampling for Ridge Regression via Regularized Volume Sampling | 2 |
| 3 | t-Exponential Triplet Embedding. | 0 |
| 4 | Minimax fixed-design linear regression | 4 |
| 5 | Open Problem: Online Sabotaged Shortest Path | 2 |
| 6 | Open problem: Shifting experts on easy data | 3 |
| 7 | Minimax games with bandits | 1 |
| 8 | 31 | |
| 9 | 56 | |
| 10 | Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection | 5 |
| 11 | Adaptive Caching by Refetching | 35 |
| 12 | Barrier Boosting | 25 |
| 13 | Relative Loss Bounds for Temporal-Difference Learning | 3 |
| 14 | The Minimax Strategy for Gaussian Density Estimation. pp | 18 |
| 15 | Linear Hinge Loss and Average Margin | 67 |
| 16 | Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions | 27 |
| 17 | Exponentially many local minima for single neurons | 58 |
| 18 | Proceedings of the seventh annual conference on Computational learning theory | 4 |
| 19 | Using Experts for Predicting Continuous Outcomes | 16 |
| 20 | Predicting {0,1}-Functions on Randomly Drawn Points (Extended Abstract) | 1 |
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.