Adaptive Sparse Gaussian Process

• URL: http://arxiv.org/abs/2302.10325v2
• Date: Fri, 7 Jul 2023 18:25:00 GMT
• Title: Adaptive Sparse Gaussian Process
• Authors: Vanessa G\'omez-Verdejo, Emilio Parrado-Hern\'andez and Manel Mart\'inez-Ram\'on
• Abstract summary: We propose the first adaptive sparse Gaussian Process (GP) able to address all these issues. We first reformulate a variational sparse GP algorithm to make it adaptive through a forgetting factor. We then propose updating a single inducing point of the sparse GP model together with the remaining model parameters every time a new sample arrives.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adaptive learning is necessary for non-stationary environments where the learning machine needs to forget past data distribution. Efficient algorithms require a compact model update to not grow in computational burden with the incoming data and with the lowest possible computational cost for online parameter updating. Existing solutions only partially cover these needs. Here, we propose the first adaptive sparse Gaussian Process (GP) able to address all these issues. We first reformulate a variational sparse GP algorithm to make it adaptive through a forgetting factor. Next, to make the model inference as simple as possible, we propose updating a single inducing point of the sparse GP model together with the remaining model parameters every time a new sample arrives. As a result, the algorithm presents a fast convergence of the inference process, which allows an efficient model update (with a single inference iteration) even in highly non-stationary environments. Experimental results demonstrate the capabilities of the proposed algorithm and its good performance in modeling the predictive posterior in mean and confidence interval estimation compared to state-of-the-art approaches.

Related papers

• Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference [55.150117654242706]
  We show that model selection for computation-aware GPs trained on 1.8 million data points can be done within a few hours on a single GPU. As a result of this work, Gaussian processes can be trained on large-scale datasets without significantly compromising their ability to quantify uncertainty.
  arXiv  Detail & Related papers  (2024-11-01T21:11:48Z)
• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• Learning Unnormalized Statistical Models via Compositional Optimization [73.30514599338407]
  Noise-contrastive estimation(NCE) has been proposed by formulating the objective as the logistic loss of the real data and the artificial noise. In this paper, we study it a direct approach for optimizing the negative log-likelihood of unnormalized models.
  arXiv  Detail & Related papers  (2023-06-13T01:18:16Z)
• Faster Adaptive Federated Learning [84.38913517122619]
  Federated learning has attracted increasing attention with the emergence of distributed data. In this paper, we propose an efficient adaptive algorithm (i.e., FAFED) based on momentum-based variance reduced technique in cross-silo FL.
  arXiv  Detail & Related papers  (2022-12-02T05:07:50Z)
• Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models [7.217783736464403]
  Use of sampling-based approaches such as Markov chain Monte Carlo may become intractable when each likelihood evaluation is computationally expensive. New approaches combining variational inference with normalizing flow are characterized by a computational cost that grows only linearly with the dimensionality of the latent variable space. We propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and the weights of a neural network surrogate model.
  arXiv  Detail & Related papers  (2021-08-28T14:31:45Z)
• Adapting by Pruning: A Case Study on BERT [9.963251767416967]
  We propose a novel model adaptation paradigm, adapting by pruning, which prunes neural connections in the pre-trained model to optimise the performance on the target task. We formulate adapting-by-pruning as an optimisation problem with a differentiable loss and propose an efficient algorithm to prune the model. Results suggest that our method can prune up to 50% weights in BERT while yielding similar performance compared to the fine-tuned full model.
  arXiv  Detail & Related papers  (2021-05-07T15:51:08Z)
• Slowly Varying Regression under Sparsity [5.22980614912553]
  We present the framework of slowly hyper regression under sparsity, allowing regression models to exhibit slow and sparse variations. We suggest a procedure that reformulates as a binary convex algorithm. We show that the resulting model outperforms competing formulations in comparable times across various datasets.
  arXiv  Detail & Related papers  (2021-02-22T04:51:44Z)
• Neural Model-based Optimization with Right-Censored Observations [42.530925002607376]
  Neural networks (NNs) have been demonstrated to work well at the core of model-based optimization procedures. We show that our trained regression models achieve a better predictive quality than several baselines.
  arXiv  Detail & Related papers  (2020-09-29T07:32:30Z)
• Real-Time Regression with Dividing Local Gaussian Processes [62.01822866877782]
  Local Gaussian processes are a novel, computationally efficient modeling approach based on Gaussian process regression. Due to an iterative, data-driven division of the input space, they achieve a sublinear computational complexity in the total number of training points in practice. A numerical evaluation on real-world data sets shows their advantages over other state-of-the-art methods in terms of accuracy as well as prediction and update speed.
  arXiv  Detail & Related papers  (2020-06-16T18:43:31Z)
• FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data [59.50904660420082]
  Federated Learning (FL) has become a popular paradigm for learning from distributed data. To effectively utilize data at different devices without moving them to the cloud, algorithms such as the Federated Averaging (FedAvg) have adopted a "computation then aggregation" (CTA) model.
  arXiv  Detail & Related papers  (2020-05-22T23:07:42Z)
• 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.