Distributed Online Optimization with Byzantine Adversarial Agents. - Sourav Sahoo 001; Anand Gokhale; Rachel Kalpana Kalaimani (ACC, 2022)

Contracting Dynamics for Time-Varying Convex Optimization. - Alexander Davydov 001; Veronica Centorrino; Anand Gokhale; Giovanni Russo 002; Francesco Bullo (CoRR, 2023)

Euclidean Contractivity of Neural Networks With Symmetric Weights. - Veronica Centorrino; Anand Gokhale; Alexander Davydov 001; Giovanni Russo 002; Francesco Bullo (IEEE Control. Syst. Lett., 2023)

Contractivity of Distributed Optimization and Nash Seeking Dynamics. - Anand Gokhale; Alexander Davydov 001; Francesco Bullo (IEEE Control. Syst. Lett., 2023)

On Weakly Contracting Dynamics for Convex Optimization. - Veronica Centorrino; Alexander Davydov 001; Anand Gokhale; Giovanni Russo 002; Francesco Bullo (IEEE Control. Syst. Lett., 2024)

Proximal Gradient Dynamics: Monotonicity, Exponential Convergence, and Applications. - Anand Gokhale; Alexander Davydov 001; Francesco Bullo (IEEE Control. Syst. Lett., 2024)

Positive Competitive Networks for Sparse Reconstruction. - Veronica Centorrino; Anand Gokhale; Alexander Davydov 001; Giovanni Russo 002; Francesco Bullo (Neural Comput., 2024)

Almost sure convergence of distributed optimization with imperfect information sharing. - Hadi Reisizadeh; Anand Gokhale; Behrouz Touri; Soheil Mohajer (Autom., 2025)

Learning to Lie: Reinforcement Learning Attacks Damage Human-AI Teams and Teams of LLMs. - Abed Kareem Musaffar; Anand Gokhale; Sirui Zeng; Rasta Tadayon; Xifeng Yan; Ambuj Singh; Francesco Bullo (CoRR, 2025)

LTLCrit: A Temporal Logic-based LLM Critic for Safe and Efficient Embodied Agents. - Anand Gokhale; Vaibhav Srivastava; Francesco Bullo (CoRR, 2025)