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Publications of A. Gokhale

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. Davydov, V. Centorrino, A. Gokhale, G. Russo, and F. Bullo. Time-Varying Convex Optimization: A Contraction and Equilibrium Tracking Approach. IEEE Transactions on Automatic Control, 70(11):7446-7460, 2025. Keyword(s): Contraction Theory. [bibtex-entry]
  3. V. Centorrino, A. Davydov, A. Gokhale, G. Russo, and F. Bullo. On Weakly Contracting Dynamics for Convex Optimization. IEEE Control Systems Letters, 8:1745-1750, 2024. Keyword(s): Contraction Theory. [bibtex-entry]
  4. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Positive Competitive Networks for Sparse Reconstruction. Neural Computation, 36(6):1163–1197, 2024. Keyword(s): Contraction Theory, Neural Networks, Theoretical Neuroscience. [bibtex-entry]
  5. A. Gokhale, A. Davydov, and F. Bullo. Proximal Gradient Dynamics: Monotonicity, Exponential Convergence, and Applications. IEEE Control Systems Letters, 8:2853-2858, 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  6. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Euclidean Contractivity of Neural Networks with Symmetric Weights. IEEE Control Systems Letters, 7:1724-1729, 2023. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  7. A. Gokhale, A. Davydov, and F. Bullo. Contractivity of Distributed Optimization and Nash Seeking Dynamics. IEEE Control Systems Letters, 7:3896-3901, 2023. [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. K. Musaffar, A. Gokhale, S. Zeng, R. Tadayon, X. Yan, A. K. Singh, and F. Bullo. Learning to Lie: Adversarial Attacks on Human-AI Teams and LLMs. In International Conference on Learning Representations, 2026. PDF [bibtex-entry]
  3. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Biologically Plausible Neural Networks for Sparse Reconstruction: A Normative Framework. In Workshop “Mathematics for Artificial Intelligence and Machine Learning”, Milan, Italy, january 2024. Note: Oral Presentation. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  4. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Towards a Top/Down Normative Framework for a Biologically Plausible Explanation of Neural Circuits: Application to Sparse Reconstruction Problems. In 5th International Convention on the Mathematics of Neuroscience and AI, Rome, Italy, May 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  5. R. Marjieh, A. Gokhale, F. Bullo, and T. Griffiths. Task Allocation in Teams as a Multi-Armed Bandit. In ACM Collective Intelligence, June 2024. Note: Accepted for both poster and oral presentation. [bibtex-entry]
  6. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Contractivity of Symmetric Neural Networks for Non-negative Sparse Approximation. In CCS/Italy 2023 Italian Regional Conference on Complex Systems, Naples, Italy, october 2023. Note: Poster Presentation. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]

Miscellaneous

  1. A. Gokhale, V. Srivastava, and F. Bullo. LogicGuard: Online Temporal Logic Shielding for LLM Planners, 2025. [bibtex-entry]
  2. A. K. Musaffar, A. Gokhale, S. Zeng, R. Tadayon, X. Yan, A. K. Singh, and F. Bullo. Learning to Lie: Reinforcement Learning-Driven Adversarial Attacks on Human-AI Teams and LLMs. ICLR Workshop on Human-AI Coevolution, 2025. Web page [bibtex-entry]
  3. R. Shima, A. Gokhale, A. Davydov, and F. Bullo. Regularized Model Predictive Control via Contractivity and Implicit Lur Analysis, September 2025. Note: Working Document. [bibtex-entry]

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