Martin J. Wainwright
About
In The Last Decade
Martin J. Wainwright
224 papers receiving 17.7k citations
Hit Papers
Peers
Comparison fields: 5 of 207
- Artificial Intelligence 7.3k
- Computer Networks and Communications 5.2k
- Computer Vision and Pattern Recognition 3.9k
- Statistics and Probability 3.4k
- Computational Mechanics 3.3k
Countries citing papers authored by Martin J. Wainwright
This map shows the geographic impact of Martin J. Wainwright'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 Martin J. Wainwright with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin J. Wainwright more than expected).
Fields of papers citing papers by Martin J. Wainwright
This network shows the impact of papers produced by Martin J. Wainwright. 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 Martin J. Wainwright. The network helps show where Martin J. Wainwright may publish in the future.
Co-authorship network of co-authors of Martin J. Wainwright
This figure shows the co-authorship network connecting the top 25 collaborators of Martin J. Wainwright. A scholar is included among the top collaborators of Martin J. Wainwright 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 Martin J. Wainwright. Martin J. Wainwright is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm | 15 |
| 3 | Theoretical guarantees for EM under misspecified Gaussian mixture models | 3 |
| 4 | Log-concave sampling: Metropolis-Hastings algorithms are fast | 2 |
| 5 | A practical scheme and fast algorithm to tune the lasso with optimality guarantees | 12 |
| 6 | Asymptotic behavior of $\ell_p$-based Laplacian regularization in semi-supervised learning | 10 |
| 7 | Information-theoretic lower bounds for distributed statistical estimation with communication constraints | 11 |
| 8 | Divide and Conquer Kernel Ridge Regression | 68 |
| 9 | Information-theoretic lower bounds for distributed statistical estimation with communication constraints | 75 |
| 10 | Local Privacy and Statistical Minimax Rates | 2 |
| 11 | 111 | |
| 12 | Stochastic Belief Propagation: Low-Complexity Message-Passing with Guarantees | 0 |
| 13 | 189 | |
| 14 | Information-theoretic lower bounds on the oracle complexity of convex optimization | 32 |
| 15 | Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness | 8 |
| 16 | Joint support recovery under high-dimensional scaling: Benefits and perils of ℓ 1,∞ -regularization | 37 |
| 17 | Phase transitions for high-dimensional joint support recovery | 14 |
| 18 | 66 | |
| 19 | Exact MAP Estimates by (Hyper)tree Agreement | 21 |
| 20 | Tree-based reparameterization for approximate inference on loopy graphs | 43 |
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.