Sham M. Kakade
- Artificial Intelligence top 0.2%
- Management Science and Operations Research top 0.2%
- Computer Vision and Pattern Recognition top 0.5%
- Cognitive Neuroscience top 2%
- Computational Theory and Mathematics top 0.5%
- Co-authors
- Peter DayanDaniel HsuJohn LangfordMatthias SeegerAndreas KrauseNiranjan SrinivasKarthik SridharanNathaniel D. Daw
- Topics
- Machine Learning and Algorithms (35 papers)Advanced Bandit Algorithms Research (30 papers)Sparse and Compressive Sensing Techniques (20 papers)
- Partner nations
- United StatesUnited KingdomIsrael
In The Last Decade
Sham M. Kakade
118 papers receiving 7.0k citations
Hit Papers
Peers
Comparison fields: 5 of 185
- Artificial Intelligence 3.6k
- Management Science and Operations Research 1.4k
- Computer Vision and Pattern Recognition 1.4k
- Cognitive Neuroscience 1.1k
- Computational Theory and Mathematics 703
Countries citing papers authored by Sham M. Kakade
This map shows the geographic impact of Sham M. Kakade'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 Sham M. Kakade with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sham M. Kakade more than expected).
Fields of papers citing papers by Sham M. Kakade
This network shows the impact of papers produced by Sham M. Kakade. 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 Sham M. Kakade. The network helps show where Sham M. Kakade may publish in the future.
Co-authorship network of co-authors of Sham M. Kakade
This figure shows the co-authorship network connecting the top 25 collaborators of Sham M. Kakade. A scholar is included among the top collaborators of Sham M. Kakade 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 Sham M. Kakade. Sham M. Kakade 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 | The Benefits of Implicit Regularization from SGD in Least Squares Problems | 1 |
| 3 | Is Long Horizon RL More Difficult Than Short Horizon RL | 1 |
| 4 | A Smoother Way to Train Structured Prediction Models | 1 |
| 5 | Accelerating Stochastic Gradient Descent | 10 |
| 6 | How to escape saddle points efficiently | 57 |
| 7 | Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent | 15 |
| 8 | Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm. | 2 |
| 9 | Tensor decompositions for learning latent variable modelsbreakdown → | 371 |
| 10 | 53 | |
| 11 | 21 | |
| 12 | Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation | 9 |
| 13 | Applications of strong convexity--strong smoothness duality to learning with matrices | 22 |
| 14 | On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization | 107 |
| 15 | High-probability regret bounds for bandit online linear optimization | 32 |
| 16 | On the Generalization Ability of Online Strongly Convex Programming Algorithms | 56 |
| 17 | Online Bounds for Bayesian Algorithms | 24 |
| 18 | Exploration in metric state spaces | 49 |
| 19 | Approximately Optimal Approximate Reinforcement Learning | 217 |
| 20 | Competitive Analysis of the Explore/Exploit Tradeoff | 0 |
About Sham M. Kakade
Sham M. Kakade is a scholar working on Computational Mathematics, Management Science and Operations Research and Artificial Intelligence, having authored 121 papers that have together received 7.4k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (35 papers), Advanced Bandit Algorithms Research (30 papers) and Sparse and Compressive Sensing Techniques (20 papers). The work is most often cited by research in Computational Mathematics (376 citations), Management Science and Operations Research (1.4k citations) and Artificial Intelligence (3.6k citations). Sham M. Kakade has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Peter Dayan, Daniel Hsu, John Langford, Matthias Seeger, Andreas Krause, Niranjan Srinivas, Karthik Sridharan, Nathaniel D. Daw, Alina Beygelzimer and Kamalika Chaudhuri. Their work appears in journals such as Nature Neuroscience, Psychological Review and IEEE Transactions on Information Theory.
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.