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Found 12 papers, 0 papers with code
Distributed Fractional Bayesian Learning for Adaptive Optimization
This paper considers a distributed adaptive optimization problem, where all agents only have access to their local cost functions with a common unknown parameter, whereas they mean to collaboratively estimate the true parameter and find the optimal solution over a connected network.
Online Parameter Identification of Generalized Non-cooperative Game
This work studies the parameter identification problem of a generalized non-cooperative game, where each player's cost function is influenced by an observable signal and some unknown parameters.
Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater's Condition
Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to $\mathcal{O}(\log(T))$, and the network cumulative constraint violation bound is reduced to $\mathcal{O}(\sqrt{\log(T)T})$ and $\mathcal{O}(\log(T))$ without and with Slater's condition, respectively.
Global Nash Equilibrium in Non-convex Multi-player Game: Theory and Algorithms
This paper takes conjugate transformation to the formulation of non-convex multi-player games, and casts the complementary problem into a variational inequality (VI) problem with a continuous pseudo-gradient mapping.
A Survey of Decision Making in Adversarial Games
Game theory has by now found numerous applications in various fields, including economics, industry, jurisprudence, and artificial intelligence, where each player only cares about its own interest in a noncooperative or cooperative manner, but without obvious malice to other players.
No-regret learning for repeated non-cooperative games with lossy bandits
This paper considers no-regret learning for repeated continuous-kernel games with lossy bandit feedback.
Small-Gain Theorem for Safety Verification under High-Relative-Degree Constraints
This paper develops a small-gain technique for the safety analysis and verification of interconnected systems with high-relative-degree safety constraints.
Multi-agent consensus over time-invariant and time-varying signed digraphs via eventual positivity
For the time-varying case, convergence to consensus can be guaranteed by the existence of a common Lyapunov function for all the signed Laplacians.
No-regret Online Learning over Riemannian Manifolds
We consider online optimization over Riemannian manifolds, where a learner attempts to minimize a sequence of time-varying loss functions defined on Riemannian manifolds.
On Faster Convergence of Scaled Sign Gradient Descent
For the first two cases, it can be shown that the scaled signGD converges at a linear rate.
Composition and Application of Current Advanced Driving Assistance System: A Review
Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price.
Online Convex Optimization Over Erdos-Renyi Random Networks
The regret bounds scaling with respect to $T$ match those obtained by state-of-the-art algorithms and fundamental limits in the corresponding centralized online optimization problems, e. g., $\mathcal{O}(\sqrt{T}) $ and $\mathcal{O}(\ln(T)) $ regrets are established for convex and strongly convex losses with full gradient feedback and two-points information, respectively.
