Daniel Hsu
- Computational Mathematics top 0.2%
- Tensor decomposition and applications 8
- Artificial Intelligence top 0.5%
- Machine Learning and Algorithms 25
- Bayesian Methods and Mixture Models 9
- Algorithms and Data Compression 7
- Gaussian Processes and Bayesian Inference 7
- Statistics and Probability top 1%
- Statistical Methods and Inference 15
- Signal Processing top 2%
- Blind Source Separation Techniques 11
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- Sparse and Compressive Sensing Techniques 14
- Co-authors
- Sham M. KakadeSanjoy DasguptaMikhail BelkinSoumik MandalSiyuan MaAnimashree AnandkumarRong GeMiguel Figueroa
- Journals
- Journal of Machine Learning Research (4 papers)Physical review. D (3 papers)Transactions of the Association for Computational Linguistics (2 papers)
- Partner nations
- United StatesUnited KingdomSingapore
In The Last Decade
Daniel Hsu
84 papers receiving 3.8k citations
Hit Papers
Peers
Comparison fields: 5 of 178
- Computational Mathematics 356
- Artificial Intelligence 2.2k
- Statistics and Probability 329
- Computer Vision and Pattern Recognition 703
- Signal Processing 342
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-authorship network
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
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2023 | 1 | |
| 3 | 2022 | 6 | |
| 4 | 2020 | 30 | |
| 5 | Correcting the bias in least squares regression with volume-rescaled sampling | 2019 | 2 |
| 6 | 2019 | 69 | |
| 7 | Teaching a black-box learner | 2019 | 6 |
| 8 | Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate | 2018 | 15 |
| 9 | Reconciling modern machine learning and the bias-variance trade-off | 2018 | 34 |
| 10 | Linear regression without correspondence | 2017 | 16 |
| 11 | Loss minimization and parameter estimation with heavy tails | 2016 | 25 |
| 12 | Global Analysis of Expectation Maximization for Mixtures of Two Gaussians | 2016 | 16 |
| 13 | Tensor decompositions for learning latent variable modelsbreakdown → | 2014 | 371 |
| 14 | 2014 | 53 | |
| 15 | Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation | 2012 | 9 |
| 16 | Sample Complexity Bounds for Differentially Private Learning. | 2011 | 15 |
| 17 | Monte Carlo Value Iteration with Macro-Actions | 2011 | 29 |
| 18 | An online learning-based framework for tracking | 2010 | 2 |
| 19 | What makes some POMDP problems easy to approximate | 2007 | 47 |
| 20 | Adaptive Quantization and Density Estimation in Silicon | 2002 | 5 |
About Daniel Hsu
Daniel Hsu is a scholar working on Computational Mathematics, Statistics and Probability and Artificial Intelligence, having authored 86 papers that have together received 4.0k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (25 papers), Statistical Methods and Inference (15 papers), Sparse and Compressive Sensing Techniques (14 papers), Blind Source Separation Techniques (11 papers), Bayesian Methods and Mixture Models (9 papers), Tensor decomposition and applications (8 papers), Algorithms and Data Compression (7 papers) and Gaussian Processes and Bayesian Inference (7 papers). The work is most often cited by research in Computational Mathematics (356 citations), Artificial Intelligence (2.2k citations) and Statistics and Probability (329 citations). Daniel Hsu has collaborated with scholars based in United States, United Kingdom and Singapore. Frequent co-authors include Sham M. Kakade, Sanjoy Dasgupta, Mikhail Belkin, Soumik Mandal, Siyuan Ma, Animashree Anandkumar, Rong Ge, Miguel Figueroa, C. Diorio and Matus Telgarsky. Their work appears in journals such as Journal of Machine Learning Research, Physical review. D, Transactions of the Association for Computational Linguistics, IEEE Journal of Solid-State Circuits 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.