Latent Gaussian Model Boosting

• URL: http://arxiv.org/abs/2105.08966v2
• Date: Fri, 21 May 2021 13:42:12 GMT
• Title: Latent Gaussian Model Boosting
• Authors: Fabio Sigrist
• Abstract summary: Tree-boosting shows excellent predictive accuracy on many data sets. We obtain increased predictive accuracy compared to existing approaches in both simulated and real-world data experiments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Latent Gaussian models and boosting are widely used techniques in statistics and machine learning. Tree-boosting shows excellent predictive accuracy on many data sets, but potential drawbacks are that it assumes conditional independence of samples, produces discontinuous predictions for, e.g., spatial data, and it can have difficulty with high-cardinality categorical variables. Latent Gaussian models, such as Gaussian process and grouped random effects models, are flexible prior models that allow for making probabilistic predictions. However, existing latent Gaussian models usually assume either a zero or a linear prior mean function which can be an unrealistic assumption. This article introduces a novel approach that combines boosting and latent Gaussian models in order to remedy the above-mentioned drawbacks and to leverage the advantages of both techniques. We obtain increased predictive accuracy compared to existing approaches in both simulated and real-world data experiments.

Related papers

• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• von Mises Quasi-Processes for Bayesian Circular Regression [57.88921637944379]
  We explore a family of expressive and interpretable distributions over circle-valued random functions. The resulting probability model has connections with continuous spin models in statistical physics. For posterior inference, we introduce a new Stratonovich-like augmentation that lends itself to fast Markov Chain Monte Carlo sampling.
  arXiv  Detail & Related papers  (2024-06-19T01:57:21Z)
• Scaling and renormalization in high-dimensional regression [72.59731158970894]
  This paper presents a succinct derivation of the training and generalization performance of a variety of high-dimensional ridge regression models. We provide an introduction and review of recent results on these topics, aimed at readers with backgrounds in physics and deep learning.
  arXiv  Detail & Related papers  (2024-05-01T15:59:00Z)
• Fusion of Gaussian Processes Predictions with Monte Carlo Sampling [61.31380086717422]
  In science and engineering, we often work with models designed for accurate prediction of variables of interest. Recognizing that these models are approximations of reality, it becomes desirable to apply multiple models to the same data and integrate their outcomes.
  arXiv  Detail & Related papers  (2024-03-03T04:21:21Z)
• Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models [69.22568644711113]
  We introduce probabilistic unrolling, a method that combines Monte Carlo sampling with iterative linear solvers to circumvent matrix inversions. Our theoretical analyses reveal that unrolling and backpropagation through the iterations of the solver can accelerate gradient estimation for maximum likelihood estimation. In experiments on simulated and real data, we demonstrate that probabilistic unrolling learns latent Gaussian models up to an order of magnitude faster than gradient EM, with minimal losses in model performance.
  arXiv  Detail & Related papers  (2023-06-05T21:08:34Z)
• Robust Gaussian Process Regression with Huber Likelihood [2.7184224088243365]
  We propose a robust process model in the Gaussian process framework with the likelihood of observed data expressed as the Huber probability distribution. The proposed model employs weights based on projection statistics to scale residuals and bound the influence of vertical outliers and bad leverage points on the latent functions estimates.
  arXiv  Detail & Related papers  (2023-01-19T02:59:33Z)
• Correcting Model Bias with Sparse Implicit Processes [0.9187159782788579]
  We show that Sparse Implicit Processes (SIP) is capable of correcting model bias when the data generating mechanism differs strongly from the one implied by the model. We use synthetic datasets to show that SIP is capable of providing predictive distributions that reflect the data better than the exact predictions of the initial, but wrongly assumed model.
  arXiv  Detail & Related papers  (2022-07-21T18:00:01Z)
• Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output [1.4298334143083322]
  Likelihood-free inference for simulator-based statistical models has attracted a surge of interest, both in the machine learning and statistics communities. Here we derive a set of theoretical results to enable estimation, hypothesis testing and construction of confidence intervals for model parameters using computation properties of the Jensen-Shannon- divergence. Such approximation offers a rapid alternative to more-intensive approaches and can be attractive for diverse applications of simulator-based models.
  arXiv  Detail & Related papers  (2022-05-22T18:00:13Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)
• Gaussian Process Regression with Local Explanation [28.90948136731314]
  We propose GPR with local explanation, which reveals the feature contributions to the prediction of each sample. In the proposed model, both the prediction and explanation for each sample are performed using an easy-to-interpret locally linear model. For a new test sample, the proposed model can predict the values of its target variable and weight vector, as well as their uncertainties.
  arXiv  Detail & Related papers  (2020-07-03T13:22:24Z)
• Gaussian Process Boosting [13.162429430481982]
  We introduce a novel way to combine boosting with Gaussian process and mixed effects models. We obtain increased prediction accuracy compared to existing approaches on simulated and real-world data sets.
  arXiv  Detail & Related papers  (2020-04-06T13:19:54Z)

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.