World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
9418
World Ranking
7502
National Ranking
3265

Overview

Philip M. Long is a researcher affiliated with Google in the United States. Their work primarily spans the field of Computer Science, with a strong emphasis on Artificial Intelligence and related subfields.

Long's scholarly contributions include a total of 44 publications in Computer Science. Within this broad area, the main subfields they have contributed to are:

  • Artificial Intelligence (37 publications)
  • Statistics and Probability (7 publications)
  • Computer Vision and Pattern Recognition (5 publications)
  • Computational Mechanics (4 publications)
  • Control and Systems Engineering (2 publications)

The main research topics covered by Long's publications include:

  • Stochastic Gradient Optimization Techniques (20 publications)
  • Machine Learning and Algorithms (14 publications)
  • Statistical Methods and Inference (10 publications)
  • Sparse and Compressive Sensing Techniques (8 publications)
  • Advanced Neural Network Applications (6 publications)
  • Adversarial Robustness in Machine Learning (6 publications)
  • Machine Learning and Data Classification (6 publications)

Long has published extensively in various venues. Frequent publication venues include:

  • arXiv (Cornell University) with 14 publications
  • Proceedings of the National Academy of Sciences (1 publication)
  • Bernoulli (1 publication)
  • Neural Computation (1 publication)
  • SIAM Journal on Discrete Mathematics (1 publication)

Recent papers authored or coauthored by Long include the following:

  • Benign overfitting in linear regression, 2020, Proceedings of the National Academy of Sciences
  • Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime, 2020, arXiv (Cornell University)
  • Failures of model-dependent generalization bounds for least-norm interpolation, 2020, arXiv (Cornell University)
  • On the Global Convergence of Training Deep Linear ResNets, 2020, arXiv (Cornell University)
  • Oracle lower bounds for stochastic gradient sampling algorithms, 2022, Bernoulli

Frequent collaborators in Long's research efforts include:

  • Peter L. Bartlett (11 joint publications)
  • Niladri S. Chatterji (9 joint publications)
  • Rocco A. Servedio (2 joint publications)
  • Gábor Lugosi (1 joint publication)
  • Alexander Tsigler (1 joint publication)

Best Publications

  • Breast cancer classification and prognosis based on gene expression profiles from a population-based study

    Christos Sotiriou;Soek-Ying Neo;Lisa M McShane;Edward L Korn

  • Benign overfitting in linear regression

    Peter L. Bartlett;Philip M. Long;Gábor Lugosi;Alexander Tsigler

  • The Relaxed Online Maximum Margin Algorithm

    Yi Li;Philip M. Long

  • Performance guarantees for hierarchical clustering

    Sanjoy Dasgupta;Philip M. Long

  • Random classification noise defeats all convex potential boosters

    Philip M. Long;Rocco A. Servedio

  • TRACKING DRIFTING CONCEPTS BY MINIMIZING DISAGREEMENTS

    David P. Helmbold;Philip M. Long

  • Optimal gene expression analysis by microarrays

    Lance D. Miller;Philip M. Long;Philip M. Long;Limsoon Wong;Sayan Mukherjee

  • On the difficulty of approximately maximizing agreements

    Shai Ben-David;Nadav Eiron;Philip M. Long

  • Improved Bounds on the Sample Complexity of Learning

    Yi Li;Philip M. Long;Aravind Srinivasan

  • Worst-case quadratic loss bounds for prediction using linear functions and gradient descent

    N. Cesa-Bianchi;P.M. Long;M.K. Warmuth

  • Fat-shattering and the learnability of real-valued functions

    Peter L. Bartlett;Philip M. Long;Robert C. Williamson

  • The Singular Values of Convolutional Layers

    Hanie Sedghi;Vineet Gupta;Philip M. Long

  • Characterizations of Learnability for Classes of {0, ..., n)-Valued Functions

    S. Bendavid;N. Cesabianchi;D. Haussler;P.M. Long

  • The Power of Localization for Efficiently Learning Linear Separators with Noise

    Pranjal Awasthi;Maria Florina Balcan;Philip M. Long

  • PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples

    Philip M. Long;Lei Tan

  • Identification of discriminators of hepatoma by gene expression profiling using a minimal dataset approach.

    Soek Ying Neo;Chon Kar Leow;Vinsensius B. Vega;Philip M. Long

  • Active and passive learning of linear separators under log-concave distributions

    Maria Florina Balcan;Philip M. Long

  • Learning Halfspaces with Malicious Noise

    Adam R. Klivans;Philip M. Long;Rocco A. Servedio

  • Tracking drifting concepts by minimizing disagreements

    Unknown

  • Characterizations of learnability for classes of {O, …, n}-valued functions

    Shai Ben-David;Nicolò Cesa-Bianchi;Philip M. Long

  • Reinforcement Learning with Immediate Rewards and Linear Hypotheses

    Naoki Abe;Alan W. Biermann;Philip M. Long

  • Associative Reinforcement Learning using Linear Probabilistic Concepts

    Naoki Abe;Philip M. Long

Frequent Co-Authors

Rocco A. Servedio
Rocco A. Servedio Columbia University
David P. Helmbold
David P. Helmbold University of California, Santa Cruz
Peter L. Bartlett
Peter L. Bartlett University of California, Berkeley
Jeffrey Scott Vitter
Jeffrey Scott Vitter University of Mississippi
Peter Auer
Peter Auer University of Leoben
Maria-Florina Balcan
Maria-Florina Balcan Carnegie Mellon University
Edison T. Liu
Edison T. Liu The Jackson Laboratory
Manfred K. Warmuth
Manfred K. Warmuth Google (United States)
Aravind Srinivasan
Aravind Srinivasan University of Maryland, College Park
Shai Ben-David
Shai Ben-David University of Waterloo

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

If you’re considering a future in Computer Science, it’s helpful to explore related fields and online degree options. Computer Science skills easily complement environmental, engineering, and technology sectors, broadening your career pathways.

For example, those curious about sustainability and environmental impact may want to learn what can you do with an environmental studies degree. This area often intersects with data science and computer modeling to address real-world issues.

Flexibility is important for many students, and there are various online computer science degree programs that accelerate learning and make study more accessible. These can provide the same rigorous training as campus programs, preparing you for software development, cybersecurity, AI, and beyond.

Technical fields like environmental engineering degree and online mechanical engineering degrees also offer affordable, accredited online options. Graduates can pursue roles that blend engineering with computing, automation, or sustainability.

Exploring degree combinations and related disciplines will help you design a career path that’s both practical and impactful in a rapidly evolving job market.

Best Scientists Citing Philip M. Long

Trending Scientists

Recently Published Articles