lyapunov function
Continuous-time Models for Stochastic Optimization Algorithms
Antonio Orvieto, Aurelien Lucchi
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AlmostSurelyStableDeepDynamics
We introduce a method for learning provably stable deep neural network based dynamic models from observed data.
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A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
A common feature of these applications is that there are multiple decision makers interacting with each other in a common environment.
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Deep Learning for Computing Convergence Rates of Markov Chains
Markovian models, estimating the rate of convergence to equilibrium is critical.
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A Lyapunov-based Approach to Safe Reinforcement Learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, Mohammad Ghavamzadeh
Neural Information Processing Systems http://nips.cc/
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Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control
Neural Information Processing Systems http://nips.cc/
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