Importance sampling for online variational learning

• URL: http://arxiv.org/abs/2402.02859v1
• Date: Mon, 5 Feb 2024 10:18:47 GMT
• Title: Importance sampling for online variational learning
• Authors: Mathis Chagneux (IP Paris), Pierre Gloaguen (UBS), Sylvain Le Corff (LPSM (UMR\_8001), SU), Jimmy Olsson (KTH)
• Abstract summary: This article addresses online variational estimation in state-space models. We focus on learning the smoothing distribution, i.e. the joint distribution of the latent states given the observations, using a variational approach together with Monte Carlo importance sampling.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article addresses online variational estimation in state-space models. We focus on learning the smoothing distribution, i.e. the joint distribution of the latent states given the observations, using a variational approach together with Monte Carlo importance sampling. We propose an efficient algorithm for computing the gradient of the evidence lower bound (ELBO) in the context of streaming data, where observations arrive sequentially. Our contributions include a computationally efficient online ELBO estimator, demonstrated performance in offline and true online settings, and adaptability for computing general expectations under joint smoothing distributions.

Related papers

• Efficient Online Variational Estimation via Monte Carlo Sampling [10.507384332827039]
  This article addresses online variational estimation in parametric state-space models.<n>We propose a new procedure for efficiently computing the evidence lower bound and its gradient in a streaming-data setting, where observations arrive sequentially.<n>It is based on i.i.d. Monte Carlo sampling, coupled with a well-chosen deep architecture, enabling both computational efficiency and flexibility.
  arXiv  Detail & Related papers  (2026-02-06T10:20:20Z)
• Nonparametric Data Attribution for Diffusion Models [57.820618036556084]
  Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs.<n>We propose a nonparametric attribution method that operates entirely on data, measuring influence via patch-level similarity between generated and training images.
  arXiv  Detail & Related papers  (2025-10-16T03:37:16Z)
• Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach [14.958884168060097]
  We present a novel approach for test-time adaptation via online self-training. Our approach combines concepts in betting martingales and online learning to form a detection tool capable of reacting to distribution shifts. Experimental results demonstrate that our approach improves test-time accuracy under distribution shifts while maintaining accuracy and calibration in their absence.
  arXiv  Detail & Related papers  (2024-08-14T12:40:57Z)
• Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models [88.47454470043552]
  We consider the problem of online fine tuning the parameters of a language model at test time, also known as dynamic evaluation. Online adaptation turns parameters into temporally changing states and provides a form of context-length extension with memory in weights.
  arXiv  Detail & Related papers  (2024-03-03T14:03:48Z)
• Uncertainty Quantification via Stable Distribution Propagation [60.065272548502]
  We propose a new approach for propagating stable probability distributions through neural networks. Our method is based on local linearization, which we show to be an optimal approximation in terms of total variation distance for the ReLU non-linearity.
  arXiv  Detail & Related papers  (2024-02-13T09:40:19Z)
• Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers [143.6249073384419]
  In this paper, we propose a distributed sampling scheme based on the alternating direction method of multipliers. We provide both theoretical guarantees of our algorithm's convergence and experimental evidence of its superiority to the state-of-the-art. In simulation, we deploy our algorithm on linear and logistic regression tasks and illustrate its fast convergence compared to existing gradient-based methods.
  arXiv  Detail & Related papers  (2024-01-29T02:08:40Z)
• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• Distribution Shift Inversion for Out-of-Distribution Prediction [57.22301285120695]
  We propose a portable Distribution Shift Inversion algorithm for Out-of-Distribution (OoD) prediction. We show that our method provides a general performance gain when plugged into a wide range of commonly used OoD algorithms.
  arXiv  Detail & Related papers  (2023-06-14T08:00:49Z)
• Particle-based Online Bayesian Sampling [24.290436348629452]
  We study an Online Particle-based Variational Inference (OPVI) algorithm that uses a set of particles to represent the approximating distribution. To reduce the gradient error caused by the use of approximation, we include a sublinear increasing batch-size method to reduce the variance. Experiments show that the proposed algorithm achieves better results than naively applying existing Bayesian sampling methods in the online setting.
  arXiv  Detail & Related papers  (2023-02-28T17:46:32Z)
• A Deep Value-network Based Approach for Multi-Driver Order Dispatching [55.36656442934531]
  We propose a deep reinforcement learning based solution for order dispatching. We conduct large scale online A/B tests on DiDi's ride-dispatching platform. Results show that CVNet consistently outperforms other recently proposed dispatching methods.
  arXiv  Detail & Related papers  (2021-06-08T16:27:04Z)
• CRPS Learning [0.0]
  Combination and aggregation techniques can improve forecast accuracy substantially. We discuss pointwise online aggregation algorithms that optimize with respect to the continuous ranked probability score (CRPS)
  arXiv  Detail & Related papers  (2021-02-01T16:54:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.