Two-Timescale Stochastic Approximation for Bilevel Optimisation Problems in Continuous-Time Models
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Abstract (arXiv)
We analyse the asymptotic properties of a continuous-time, two-timescale stochastic approximation algorithm designed for stochastic bilevel optimisation problems in continuous-time models. We obtain the weak convergence rate of this algorithm in the form of a central limit theorem. We also demonstrate how this algorithm can be applied to several continuous-time bilevel optimisation problems.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 45 references that are in the map. First posted on arXiv 14 Jun 2022.
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22 of 45 references.
References · 22
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- 2020Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performancecited by 35
10 more
- 2021Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithmcited by 26
- 2018Multi-Level Stochastic Gradient Methods for Nested Composition Optimizationcited by 24
- 2019Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximationcited by 18
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- 2017Stochastic Gradient Descent in Continuous Time: A Central Limit Theoremcited by 10
- 2021Parameter Estimation for the McKean-Vlasov Stochastic Differential Equationcited by 8
- 2016Online Maximum Likelihood Estimation of the Parameters of Partially Observed Diffusion Processescited by 6
- 2020Two-Timescale Stochastic Gradient Descent in Continuous Time with Applications to Joint Online Parameter Estimation and Optimal Sensor Placementcited by 6
- 2021Multimodal Meta-Learning for Time Series Regressioncited by 6
- 2020On stochastic mirror descent with interacting particles: convergence properties and variance reductioncited by 3
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Citing papers per year: 2026: 2.
2 papers.