Publications of A. V. Proskurnikov

Articles in journal or book chapters

1. A. Gokhale, A. V. Proskurnikov, Y. Kawano, and F. Bullo. A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning. IEEE Transactions on Automatic Control, April 2026. Note: Submitted. [bibtex-entry]
2. A. V. Proskurnikov and F. Bullo. Regular Pairings for Non-Quadratic Lyapunov Functions and Contraction Analysis. SIAM Journal on Control and Optimization, 64(1):496-521, 2026. Keyword(s): Contraction Theory. [bibtex-entry]
3. A. Davydov, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Contraction Analysis of Continuous-Time Neural Networks. IEEE Transactions on Automatic Control, 70(1):235-250, 2025. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
4. A. Davydov, S. Jafarpour, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Monotone Operator Theory and Applications. Journal of Machine Learning Research, 25(307):1-33, 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
5. A. V. Proskurnikov, A. Davydov, and F. Bullo. The Yakubovich S-Lemma Revisited: Stability and Contractivity in Non-Euclidean Norms. SIAM Journal on Control and Optimization, 61(4):1955-1978, 2023. Keyword(s): Contraction Theory. [bibtex-entry]
6. N. E. Friedkin, A. V. Proskurnikov, and F. Bullo. Group Dynamics on Multidimensional Attitudes. Social Networks, 65:157-167, 2021. [bibtex-entry]
7. P. Cisneros-Velarde, N. E. Friedkin, A. V. Proskurnikov, and F. Bullo. Structural Balance via Gradient Flows over Signed Graphs. IEEE Transactions on Automatic Control, 66(7):3169-3183, 2020. [bibtex-entry]
8. N. E. Friedkin, W. Mei, A. V. Proskurnikov, and F. Bullo. Mathematical Structures in Group Decision-Making on Resource Allocation Distributions. Scientific Reports, 9(1):1377, 2019. Keyword(s): Social Networks. [bibtex-entry]
9. N. E. Friedkin, A. V. Proskurnikov, and F. Bullo. Positive Contagion and the Macrostructures of Generalized Balance. Network Science, 7(4):445-458, 2019. [bibtex-entry]

Conference articles

1. A. Gokhale, A. V. Proskurnikov, Y. Kawano, and F. Bullo. Contracting Neural Networks: Sharp LMI Conditions with Applications to Integral Control and Deep Learning. In IEEE Conf. on Decision and Control, Honolulu, Hawaii, 2026. Note: To appear. [bibtex-entry]
2. A. Davydov, S. Jafarpour, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Monotone Operator Theory with Applications to Recurrent Neural Networks. In IEEE Conf. on Decision and Control, Cancun, Mexico, December 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
3. A. Davydov, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Contractivity of Recurrent Neural Networks. In American Control Conference, Atlanta, USA, pages 1527-1534, May 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
4. S. Jafarpour, A. Davydov, A. V. Proskurnikov, and F. Bullo. Robust Implicit Networks via Non-Euclidean Contractions. In Advances in Neural Information Processing Systems, volume 34, pages 9857--9868, December 2021. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]