Stefano Ermon
About
In The Last Decade
Stefano Ermon
29 papers receiving 194 citations
Peers
Comparison fields: 5 of 66
- Artificial Intelligence 99
- Computer Vision and Pattern Recognition 34
- Computer Networks and Communications 21
- Computational Theory and Mathematics 20
- Statistical and Nonlinear Physics 19
Countries citing papers authored by Stefano Ermon
This map shows the geographic impact of Stefano Ermon'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 Stefano Ermon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefano Ermon more than expected).
Fields of papers citing papers by Stefano Ermon
This network shows the impact of papers produced by Stefano Ermon. 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 Stefano Ermon. The network helps show where Stefano Ermon may publish in the future.
Co-authorship network of co-authors of Stefano Ermon
This figure shows the co-authorship network connecting the top 25 collaborators of Stefano Ermon. A scholar is included among the top collaborators of Stefano Ermon 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 Stefano Ermon. Stefano Ermon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 12 | |
| 3 | 19 | |
| 4 | 3 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | Imitation with Neural Density Models | 3 |
| 8 | Output Diversified Initialization for Adversarial Attacks | 1 |
| 9 | Stochastic Optimization of Sorting Networks via Continuous Relaxations | 10 |
| 10 | Adaptive Antithetic Sampling for Variance Reduction | 2 |
| 11 | Bayesian optimization and attribute adjustment | 2 |
| 12 | Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance | 5 |
| 13 | Accelerating Natural Gradient with Higher-Order Invariance | 1 |
| 14 | Sparse Gaussian processes for Bayesian optimization | 21 |
| 15 | Tight Variational Bounds via Random Projections and I-Projections | 2 |
| 16 | 3 | |
| 17 | Density Propagation and Improved Bounds on the Partition Function | 1 |
| 18 | 10 | |
| 19 | Accelerated Adaptive Markov Chain for Partition Function Computation | 6 |
| 20 | 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.