paper-with-me

Papers

Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent

2023-02-14 · Benjamin Gess, Sebastian Kassing, Vitalii Konarovskyi

We propose new limiting dynamics for stochastic gradient descent in the small learning rate regime called stochastic modified flows. These SDEs are driven by a cylindrical Brownian motion and improve the so-called stochastic modified equations by having regular diffusion coefficients and by matching the multi-point statistics. As a second contribution, we introduce distribution dependent stochastic modified flows which we prove to describe the fluctuating limiting dynamics of stochastic gradient descent in the small learning rate - infinite width scaling regime.

📄 PDF Abstract BibTeX arXiv:2302.07125

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Diffusion Diffusion models generate samples by gradually removing noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound…

Similar Papers 제목 키워드 기반

PIONM: A Generalized Approach to Solving Density-Constrained Mean-Field Games Equilibrium under Modified Boundary Conditions

2025-04-04 · Jinwei Liu, Wang Yao, Xiao Zhang

Neural network-based methods are effective for solving equilibria in Mean-Field Games (MFGs), particularly in high-dimensional settings. However, solving the coupled partial differential equations (PDEs) in MFGs limits t…

Conservative SPDEs as fluctuating mean field limits of stochastic gradient descent

2022-07-12 · Benjamin Gess, Rishabh S. Gvalani, Vitalii Konarovskyi

The convergence of stochastic interacting particle systems in the mean-field limit to solutions of conservative stochastic partial differential equations is established, with optimal rate of convergence. As a second main…

Implicit regularisation in stochastic gradient descent: from single-objective to two-player games

2023-07-11 · Mihaela Rosca, Marc Peter Deisenroth

Recent years have seen many insights on deep learning optimisation being brought forward by finding implicit regularisation effects of commonly used gradient-based optimisers. Understanding implicit regularisation can no…

Structured Stochastic Variational Inference

2014-04-16 · Matthew D. Hoffman, David M. Blei

Stochastic variational inference makes it possible to approximate posterior distributions induced by large datasets quickly using stochastic optimization. The algorithm relies on the use of fully factorized variational d…

SensitivityStochastic OptimizationVariational Inference

Nonlinear reserving and multiple contract modifications in life insurance

2020-03-27

Life insurance cash flows become reserve dependent when contract conditions are modified during the contract term on condition that actuarial equivalence is maintained. As a result, insurance cash flows and prospective r…