Daniel Hsu
Impact in
- Computational Mathematics top 0.2%
- Tensor decomposition and applications
- Artificial Intelligence top 0.5%
- Machine Learning and Algorithms
- Adversarial Robustness in Machine Learning
- Stochastic Gradient Optimization Techniques
- Privacy-Preserving Technologies in Data
- Neural Networks and Applications
Papers in
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- Machine Learning and Algorithms 10
- Stochastic Gradient Optimization Techniques 7
- Bayesian Methods and Mixture Models 7
- Privacy-Preserving Technologies in Data 6
- Topic Modeling 4
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- Statistical Methods and Inference 11
- Advanced Statistical Methods and Models 5
- Co-authors
- Sham M. Kakade (17 shared papers)Mikhail Belkin (2 shared papers)Tong Zhang (8 shared papers)Siyuan Ma (1 shared paper)Sanjoy Dasgupta (3 shared papers)Animashree Anandkumar (6 shared papers)Miguel E. Figueroa (3 shared papers)Chris Diorio (3 shared papers)
- Journals
- Physical review. D (3 papers)Electronic Communications in Probability (2 papers)Transactions of the Association for Computational Linguistics (2 papers)IEEE Transactions on Information Theory (2 papers)Monthly Notices of the Royal Astronomical Society (1 paper)
- Partner nations
- United StatesGermanyHungary
In The Last Decade
Daniel Hsu
67 papers receiving 4.6k citations
Daniel Hsu's Hit Papers
Peers
Comparison fields: 5 of 166
- Computational Mathematics 326
- Artificial Intelligence 2.2k
- Statistics and Probability 378
- Computer Vision and Pattern Recognition 410
- Safety Research 171
Countries citing papers authored by Daniel Hsu
This map shows the geographic impact of Daniel Hsu'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 Daniel Hsu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Hsu more than expected).
Fields of papers citing papers by Daniel Hsu
This network shows the impact of papers produced by Daniel Hsu. 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 Daniel Hsu. The network helps show where Daniel Hsu may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Hsu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 67 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Reconciling modern machine-learning practice and the classical bias–variance trade-off Hit paper breakdown → | 2019 | 890 |
| 2 | Tensor decompositions for learning latent variable models Hit paper breakdown → | 2014 | 426 |
| 3 | Certified Robustness to Adversarial Examples with Differential Privacy Hit paper breakdown → | 2019 | 418 |
| 4 | 2002 | 342 | |
| 5 | 2008 | 313 | |
| 6 | Multi-Label Prediction via Compressed Sensing | 2009 | 210 |
| 7 | 2004 | 188 | |
| 8 | 2012 | 172 | |
| 9 | 2012 | 134 | |
| 10 | 2011 | 132 | |
| 11 | 2017 | 130 | |
| 12 | 2013 | 119 | |
| 13 | 2018 | 100 | |
| 14 | 2014 | 94 | |
| 15 | 2020 | 92 | |
| 16 | 2019 | 84 | |
| 17 | 2018 | 71 | |
| 18 | 2013 | 66 | |
| 19 | What makes some POMDP problems easy to approximate | 2007 | 52 |
| 20 | 2014 | 48 |
About Daniel Hsu
Daniel Hsu is a scholar working on Artificial Intelligence, Statistics and Probability, Computational Mathematics, Astronomy and Astrophysics and Management Science and Operations Research, having authored 67 papers that have together received 4.8k indexed citations. Recurring topics across this work include Statistical Methods and Inference (11 papers), Machine Learning and Algorithms (10 papers), Stochastic Gradient Optimization Techniques (7 papers), Bayesian Methods and Mixture Models (7 papers), Privacy-Preserving Technologies in Data (6 papers), Tensor decomposition and applications (5 papers), Advanced Statistical Methods and Models (5 papers) and Topic Modeling (4 papers). The work is most often cited by research in Computational Mathematics (326 citations), Artificial Intelligence (2.2k citations), Statistics and Probability (378 citations), Computer Vision and Pattern Recognition (410 citations) and Safety Research (171 citations). Daniel Hsu has collaborated with scholars based in United States, Germany and Hungary. Frequent co-authors include Sham M. Kakade, Mikhail Belkin, Tong Zhang, Siyuan Ma, Sanjoy Dasgupta, Animashree Anandkumar, Miguel E. Figueroa, Chris Diorio, Roxana Geambasu and Vaggelis Atlidakis. Their work appears in journals such as Physical review. D, Electronic Communications in Probability, Transactions of the Association for Computational Linguistics, IEEE Transactions on Information Theory and Monthly Notices of the Royal Astronomical Society.
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.