Constantinos Daskalakis
About
In The Last Decade
Constantinos Daskalakis
96 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 111
- Management Science and Operations Research 1.3k
- Economics and Econometrics 628
- Artificial Intelligence 604
- Marketing 376
- Computer Networks and Communications 370
Countries citing papers authored by Constantinos Daskalakis
This map shows the geographic impact of Constantinos Daskalakis'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 Constantinos Daskalakis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Constantinos Daskalakis more than expected).
Fields of papers citing papers by Constantinos Daskalakis
This network shows the impact of papers produced by Constantinos Daskalakis. 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 Constantinos Daskalakis. The network helps show where Constantinos Daskalakis may publish in the future.
Co-authorship network of co-authors of Constantinos Daskalakis
This figure shows the co-authorship network connecting the top 25 collaborators of Constantinos Daskalakis. A scholar is included among the top collaborators of Constantinos Daskalakis 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 Constantinos Daskalakis. Constantinos Daskalakis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Tight last-iterate convergence rates for no-regret learning in multi-player games | 1 |
| 2 | A Theoretical and Practical Framework for Regression and Classification from Truncated Samples. | 1 |
| 3 | GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences | 0 |
| 4 | Learning from weakly dependent data under Dobrushin's condition | 2 |
| 5 | 4 | |
| 6 | Learning and Testing Causal Models with Interventions | 3 |
| 7 | Ten Steps of EM Suffice for Mixtures of Two Gaussians | 11 |
| 8 | Training GANs with Optimism | 28 |
| 9 | Square Hellinger Subadditivity for Bayesian Networks and its Applications to Identity Testing | 2 |
| 10 | Optimum Statistical Estimation with Strategic Data Sources | 27 |
| 11 | Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians | 9 |
| 12 | 7 | |
| 13 | 16 | |
| 14 | An Algorithmic Characterization of Multi-Dimensional Mechanisms | 1 |
| 15 | 10 | |
| 16 | 48 | |
| 17 | Connectivity and Equilibrium in Random Games | 2 |
| 18 | 2 | |
| 19 | Three-Player Games Are Hard | 71 |
| 20 | 14 |
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.