World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
52
Citations
14596
World Ranking
4999
National Ranking
2323

Research.com Recognitions

  • 2016 - Fellow of Alfred P. Sloan Foundation

Overview

Daniel Hsu is affiliated with Columbia University in the United States and specializes primarily in the field of Computer Science. Within this domain, their work focuses largely on Artificial Intelligence, with contributions also spanning Statistics and Probability, Computer Vision and Pattern Recognition, Computational Mechanics, and Electrical and Electronic Engineering.

The research topics frequently addressed by Daniel Hsu include Sparse and Compressive Sensing Techniques, Face and Expression Recognition, Statistical Methods and Inference, Neural Networks and Applications, Machine Learning and Algorithms, Topic Modeling, and Machine Learning and Data Classification.

Daniel Hsu has published extensively in several venues. Most of their works appear in arXiv (Cornell University), with 35 publications in this venue alone. Other notable publication venues include Harvard Dataverse, Journal of Vascular and Interventional Radiology, Physical Review D, and the Journal of Statistical Mechanics Theory and Experiment.

Among their recent publications are:

  • "Classification vs regression in overparameterized regimes: Does the loss function matter?" (2020), arXiv (Cornell University)
  • "Interpreting deep learning models for weak lensing" (2020), Physical Review D
  • "On the proliferation of support vectors in high dimensions*" (2022), Journal of Statistical Mechanics Theory and Experiment
  • "Contrastive estimation reveals topic posterior information to linear models" (2020), arXiv (Cornell University)

Throughout their career, they have collaborated frequently with several researchers, including Bo Cowgill, Fabrizio Dell'Acqua, Nakul Verma, Augustin Chaintreau, and Clayton Sanford.

Daniel Hsu was recognized as a Fellow of the Alfred P. Sloan Foundation in 2016.

Best Publications

  • Reconciling modern machine-learning practice and the classical bias-variance trade-off.

    Mikhail Belkin;Daniel Hsu;Siyuan Ma;Soumik Mandal

  • Tensor decompositions for learning latent variable models

    Animashree Anandkumar;Rong Ge;Daniel Hsu;Sham M. Kakade

  • Certified Robustness to Adversarial Examples with Differential Privacy

    Mathias Lecuyer;Vaggelis Atlidakis;Roxana Geambasu;Daniel Hsu

  • Hierarchical sampling for active learning

    Sanjoy Dasgupta;Daniel Hsu

  • A spectral algorithm for learning Hidden Markov Models

    Daniel Hsu;Sham M. Kakade;Tong Zhang

  • Multi-Label Prediction via Compressed Sensing

    John Langford;Tong Zhang;Daniel J. Hsu;Sham M Kakade

  • Multi-Label Prediction via Compressed Sensing

    Daniel Hsu;Sham M. Kakade;John Langford;Tong Zhang

  • A tail inequality for quadratic forms of subgaussian random vectors

    Daniel J. Hsu;Sham M. Kakade;Tong Zhang

  • Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

    Alekh Agarwal;Daniel Hsu;Satyen Kale;John Langford

  • Learning mixtures of spherical gaussians: moment methods and spectral decompositions

    Daniel Hsu;Sham M. Kakade

  • A Method of Moments for Mixture Models and Hidden Markov Models

    Animashree Anandkumar;Daniel J. Hsu;Sham M. Kakade

  • A general agnostic active learning algorithm

    Sanjoy Dasgupta;Claire Monteleoni;Daniel J. Hsu

  • a CAPpella: programming by demonstration of context-aware applications

    Anind K. Dey;Raffay Hamid;Chris Beckmann;Ian Li

  • Two Models of Double Descent for Weak Features

    Mikhail Belkin;Daniel Hsu;Ji Xu

  • Robust Matrix Decomposition With Sparse Corruptions

    D. Hsu;S. M. Kakade;Tong Zhang

  • A Tensor Spectral Approach to Learning Mixed Membership Community Models

    Animashree Anandkumar;Rong Ge;Daniel J. Hsu;Sham M. Kakade

  • A Spectral Algorithm for Latent Dirichlet Allocation

    Animashree Anandkumar;Dean P. Foster;Daniel Hsu;Sham M. Kakade

  • Efficient optimal learning for contextual bandits

    Miroslav Dudik;Daniel Hsu;Satyen Kale;Nikos Karampatziakis

  • Random Design Analysis of Ridge Regression

    Daniel Hsu;Sham M. Kakade;Tong Zhang

  • A tensor approach to learning mixed membership community models

    Animashree Anandkumar;Rong Ge;Daniel Hsu;Sham M. Kakade

  • A General Agnostic Active Learning Algorithm.

    Sanjoy Dasgupta;Daniel J. Hsu;Claire Monteleoni

Frequent Co-Authors

Sham M. Kakade
Sham M. Kakade Harvard University
Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign
John Langford
John Langford Microsoft (United States)
Kamalika Chaudhuri
Kamalika Chaudhuri University of California, San Diego
Anima Anandkumar
Anima Anandkumar Nvidia (United Kingdom)
Sanjoy Dasgupta
Sanjoy Dasgupta University of California, San Diego
Alekh Agarwal
Alekh Agarwal Google (United States)
Mikhail Belkin
Mikhail Belkin University of California, San Diego
Dean P. Foster
Dean P. Foster Amazon (United States)
Luis Gravano
Luis Gravano Columbia University

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