mlsanityData to 7 Oct 2026

An exact multiple-time-step variational formulation for the committor and the transition rate

arXiv 2509.03539 ↗First posted 29 Aug 2025cond-mat.stat-mech cs.LG physics.comp-phcited by 1

Not on the map. Why?

Abstract (arXiv)

For a transition between two stable states, the committor is the probability that the dynamics leads to one stable state before the other. It can be estimated from trajectory data by minimizing an expression for the transition rate that depends on a lag time. We show that an existing such expression is minimized by the exact committor only when the lag time is a single time step, resulting in a biased estimate in practical applications. We introduce an alternative expression that is minimized by the exact committor at any lag time. The key idea is that, when trajectories enter the stable states, the times that they enter (stopping times) must be used for estimating the committor and transition rate instead of the lag time. Numerical tests on benchmark systems demonstrate that our committor and transition rate estimates are much less sensitive to the choice of lag time. We show how further accuracy for the transition rate can be achieved by combining results from two lag times. We also relate the transition rate expression to a variational approach for kinetic statistics based on the mean-squared residual and discuss further numerical considerations with the aid of a decomposition of the error into dynamic modes.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 56 references that are in the map. First posted on arXiv 29 Aug 2025.

Builds on

11 of 56 references.

References · 11

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  2. 2016Layer Normalizationcited by 5,579
  3. 2024Scaling Exponents Across Parameterizations and Optimizerscited by 71
  4. 2023Maintaining Plasticity in Continual Learning via Regenerative Regularizationcited by 63
  5. 2018Solving for high dimensional committor functions using artificial neural networkscited by 34
  6. 2019Computing Committor Functions for the Study of Rare Events Using Deep Learningcited by 29
  7. 2023Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event predictioncited by 9
  8. 2023A Finite Expression Method for Solving High-Dimensional Committor Problemscited by 9
  9. 2024The surprising efficiency of temporal difference learning for rare event predictioncited by 7
  10. 2024The fast committor machine: Interpretable prediction with kernelscited by 5
  11. 2024Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamicscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.