averysi224 / mlt-papers

Awesome papers in machine learning theory

MLT-Papers

Awesome papers in machine learning theory & deep learning theory.

Contents

• Machine Learning Theory
  • ML Theory Books
  • ML Theory Papers
• Deep Learning Theory
  • DL Theory Books
  • DL Theory Surveys
  • DL Theory Papers

ML Theory Books

1. Understanding Machine Learning: From Theory to Algorithms.

   • Year 2014.
   • Shai Shalev-Shwartz, Shai Ben-David.
   • book

2. Foundations of Machine Learning.

   • Year 2018.
   • Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.
   • book

3. Learning Theory from First Principles.

   • Year 2021.
   • Francis Bach.
   • book

Machine Learning Theory

1. Learnability and the Vapnik-Chervonenkis Dimension.

   • Journal of the Association for Computing Machinery 1989.
   • Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, Manfred M. Warmuth.
   • paper

2. Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension.

   • Machine Learning 1995.
   • Sally Floyd, Manfred Warmuth.
   • paper

3. Characterizations of Learnability for Classes of ${0, \dots, n}$-valued Functions.

   • Journal of Computer and System Sciences 1995.
   • Shai Ben-David, Nicolo Cesa-Bianchi, David Haussler, Philip M. Long.
   • paper

4. Scale-Sensitive Dimensions, Uniform Convergence, and Learnability.

   • JACM 1997.
   • Noga Alon, Shai Ben-David, Nicolo Cesa-Bianchi, David Haussler.
   • paper

5. Regret Bounds for Prediction Problems.

   • COLT 1999.
   • Geoffrey J. Gordon.
   • paper

6. A Study About Algorithmic Stability and Their Relation to Generalization Performances.

   • Technical Report 2000.
   • Andre Elissee.
   • paper

7. Algorithmic Stability and Generalization Performance.

   • NIPS 2001.
   • Olivier Bousquet, Andre Elisseeff.
   • paper

8. A Generalized Representer Theorem.

   • COLT 2001.
   • Bernhard Scholkopf, Ralf Herbrich, Alex J. Smola.
   • paper

9. Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms.

   • Phd Thesis 2002.
   • Olivier Bousquet.
   • paper

10. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results.

    • JMLR 2002.
    • Peter L. Bartlett, Shahar Mendelson.
    • paper

11. Stability and Generalization.

    • JMLR 2002.
    • Olivier Bousquet, Andre Elisseeff.
    • paper

12. Almost-Everywhere Algorithmic Stability and Generalization Error.

    • UAI 2002.
    • Samuel Kutin, Partha Niyogi.
    • paper

13. PAC-Bayes & Margins.

    • NIPS 2003.
    • John Langford, John Shawe-Taylor.
    • paper

14. Statistical Behavior and Consistency of Classification Methods based on Convex Risk Minimization.

    • Annals of Statistics 2004.
    • Tong Zhang.
    • paper

15. Theory of Classification: A Survey of Some Recent Advances.

    • ESAIM: Probability and Statistics 2005.
    • Stephane Boucheron, Olivier Bousquet, Gabor Lugosi.
    • paper

16. Learning Theory: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization.

    • Advances in Computational Mathematics 2006.
    • Sayan Mukherjee, Partha Niyogi, Tomaso Poggio, Ryan Rifkin.
    • paper

17. Tutorial on Practical Prediction Theory for Classification.

    • JMLR 2006.
    • John Langford.
    • paper

18. Rademacher Complexity Bounds for Non-I.I.D. Processes.

    • NIPS 2008.
    • Mehryar Mohri, Afshin Rostamizadeh.
    • paper

19. On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization.

    • NIPS 2008.
    • Sham M. Kakade, Karthik Sridharan, and Ambuj Tewari.
    • paper

20. Agnostic Online Learning.

    • COLT 2009.
    • Shai Ben-David, David Pal, Shai Shalev-Shwartz.
    • paper

21. Learnability, Stability and Uniform Convergence.

    • JMLR 2010.
    • Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan.
    • paper

22. Multiclass Learnability and the ERM Principle.

    • COLT 2011.
    • Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz.
    • paper

23. Algorithmic Stability and Hypothesis Complexity.

    • ICML 2017.
    • Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao.
    • paper

24. Stability and Generalization of Learning Algorithms that Converge to Global Optima.

    • ICML 2018.
    • Zachary Charles, Dimitris Papailiopoulos.
    • paper

25. Generalization Bounds for Uniformly Stable Algorithms.

    • NIPS 2018.
    • Vitaly Feldman, Jan Vondrak.
    • paper

26. Reconciling Modern Machine Learning Practice and the Bias-Variance Trade-Off.

    • arXiv 2019.
    • Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal.
    • paper

27. Sharper Bounds for Uniformly Stable Algorithms.

    • COLT 2020.
    • Olivier Bousquet, Yegor Klochkov, Nikita Zhivotovskiy.
    • paper

28. Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures.

    • NeurIPS 2021.
    • Yuan Cao, Quanquan Gu, Mikhail Belkin.
    • paper

Deep Learning Theory

DL Theory Books

1. The Modern Mathematics of Deep Learning.
   • arXiv 2021.
   • Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen.
   • book

DL Theory Surveys

1. Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective.

   • arXiv 2019.
   • Guan-Horng Liu, Evangelos A. Theodorou.
   • paper

2. Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation.

   • arXiv 2019.
   • Greg Yang
   • paper

DL Theory Papers

1. Regularization Algorithms for Learning that are Equivalent to Multilayer Networks.

   • Science 1990.
   • T. Poggio, F. Girosi.
   • paper

2. Strong Universal Consistency of Neural Network Classifiers.

   • IEEE Transactions on Information Theory 1993.
   • AndrAs Farag, GAbor Lugosi.
   • paper

3. For Valid Generalization, the Size of the Weights is More Important Than the Size of the Network.

   • NIPS 1996.
   • Peter L. Bartlett.
   • paper

4. Benefits of Depth in Neural Networks.

   • COLT 2016.
   • Matus Telgarsky.
   • paper

5. Understanding Deep Learning Requires Rethinking Generalization.

   • ICLR 2017.
   • Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals.
   • paper

6. Convergence Analysis of Two-layer Neural Networks with ReLU Activation.

   • arXiv 2017.
   • Yuanzhi Li, Yang Yuan.
   • paper

7. Neural Tangent Kernel: Convergence and Generalization in Neural Networks.

   • NeurIPS 2018.
   • Arthur Jacot, Franck Gabriel, Clément Hongler.
   • paper

8. PAC-Bayesian Margin Bounds for Convolutional Neural Networks.

   • arXiv 2018
   • Konstantinos Pitas, Mike Davies, Pierre Vandergheynst.
   • paper
   • code

9. To Understand Deep Learning We Need to Understand Kernel Learning.

   • ICML 2018.
   • Mikhail Belkin, Siyuan Ma, Soumik Mandal.
   • paper

10. The Vapnik–Chervonenkis Dimension of Graph and Recursive Neural Networks.

    • ML 2018.
    • Franco Scarselli, Ah Chung Tsoi, Markus Hagenbuchner.
    • paper

11. Explicitizing an Implicit Bias of the Frequency Principle in Two-layer Neural Networks.

    • arXiv 2019.
    • Yaoyu Zhang, Zhi-Qin John Xu, Tao Luo, Zheng Ma.
    • paper

12. Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers.

    • NeurIPS 2019.
    • Zeyuan Allen-Zhu, Yuanzhi Li, Yingyu Liang.
    • paper

13. Deep Learning Generalizes Because the Parameter-Function Map is Biased Towards Simple Functions.

    • ICLR 2019.
    • Guillermo Valle Pérez, Chico Q. Camargo, Ard A. Louis.
    • paper

14. Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent.

    • arXiv 2020.
    • David Holzmuller, Ingo Steinwart.
    • paper

15. On the Distance Between Two Neural Networks and the Stability of Learning.

    • Neurips 2021.
    • Jeremy Bernstein, Arash Vahdat, Yisong Yue, Ming-Yu Liu.
    • paper

16. Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets.

    • arXiv 2021.
    • Shao-Bo Lin, Yao Wang, Ding-Xuan Zhou.
    • paper

17. Learnability of Convolutional Neural Networks for Infinite Dimensional Input via Mixed and Anisotropic Smoothness.

    • ICLR 2022.
    • Sho Okumoto, Taiji Suzuki.
    • paper