Factorized Structured Regression for Large-Scale Varying Coefficient Models

• URL: http://arxiv.org/abs/2205.13080v1
• Date: Wed, 25 May 2022 23:12:13 GMT
• Title: Factorized Structured Regression for Large-Scale Varying Coefficient Models
• Authors: David R\"ugamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. M\"uller, Clemens Stachl
• Abstract summary: We propose Factorized Structured Regression (FaStR) for scalable varying coefficient models. FaStR overcomes limitations of general regression models for large-scale data by combining structured additive regression and factorization approaches in a neural network-based model implementation. Empirical results confirm that the estimation of varying coefficients of our approach is on par with state-of-the-art regression techniques.
• Score: 1.3282354370017082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facilitate highly individualized recommendations. Conceptually, many RS can be viewed as instances of statistical regression models that incorporate complex feature effects and potentially non-Gaussian outcomes. Such structured regression models, including time-aware varying coefficients models, are, however, limited in their applicability to categorical effects and inclusion of a large number of interactions. Here, we propose Factorized Structured Regression (FaStR) for scalable varying coefficient models. FaStR overcomes limitations of general regression models for large-scale data by combining structured additive regression and factorization approaches in a neural network-based model implementation. This fusion provides a scalable framework for the estimation of statistical models in previously infeasible data settings. Empirical results confirm that the estimation of varying coefficients of our approach is on par with state-of-the-art regression techniques, while scaling notably better and also being competitive with other time-aware RS in terms of prediction performance. We illustrate FaStR's performance and interpretability on a large-scale behavioral study with smartphone user data.

Related papers

• Linked shrinkage to improve estimation of interaction effects in regression models [0.0]
  We develop an estimator that adapts well to two-way interaction terms in a regression model. We evaluate the potential of the model for inference, which is notoriously hard for selection strategies. Our models can be very competitive to a more advanced machine learner, like random forest, even for fairly large sample sizes.
  arXiv  Detail & Related papers  (2023-09-25T10:03:39Z)
• Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression [53.15502562048627]
  Recent work has built the connection between self-supervised learning and the approximation of the top eigenspace of a graph Laplacian operator. This work delves into a statistical analysis of augmentation-based pretraining.
  arXiv  Detail & Related papers  (2023-06-01T15:18:55Z)
• Scalable Estimation for Structured Additive Distributional Regression [0.0]
  We propose a novel backfitting algorithm, which is based on the ideas of gradient descent and can deal virtually with any amount of data on a conventional laptop. Performance is evaluated using an extensive simulation study and an exceptionally challenging and unique example of lightning count prediction over Austria.
  arXiv  Detail & Related papers  (2023-01-13T14:59:42Z)
• A prediction and behavioural analysis of machine learning methods for modelling travel mode choice [0.26249027950824505]
  We conduct a systematic comparison of different modelling approaches, across multiple modelling problems, in terms of the key factors likely to affect model choice. Results indicate that the models with the highest disaggregate predictive performance provide poorer estimates of behavioural indicators and aggregate mode shares. It is also observed that the MNL model performs robustly in a variety of situations, though ML techniques can improve the estimates of behavioural indices such as Willingness to Pay.
  arXiv  Detail & Related papers  (2023-01-11T11:10:32Z)
• Adaptive LASSO estimation for functional hidden dynamic geostatistical model [69.10717733870575]
  We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hiddenstatistical models (f-HD) The algorithm is based on iterative optimisation and uses an adaptive least absolute shrinkage and selector operator (GMSOLAS) penalty function, wherein the weights are obtained by the unpenalised f-HD maximum-likelihood estimators.
  arXiv  Detail & Related papers  (2022-08-10T19:17:45Z)
• Dynamically-Scaled Deep Canonical Correlation Analysis [77.34726150561087]
  Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. We introduce a novel dynamic scaling method for training an input-dependent canonical correlation model.
  arXiv  Detail & Related papers  (2022-03-23T12:52:49Z)
• Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regression [17.158289775348063]
  Kernelized tensor Regression (BKTR) can be considered a new and scalable approach to modeling processes with low-rank cotemporal structure. We conduct extensive experiments on both synthetic and real-world data sets, and our results confirm the superior performance and efficiency of BKTR for model estimation and inference.
  arXiv  Detail & Related papers  (2021-08-31T19:22:23Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• An Extended Multi-Model Regression Approach for Compressive Strength Prediction and Optimization of a Concrete Mixture [0.0]
  A model based evaluation of concrete compressive strength is of high value, both for the purpose of strength prediction and the mixture optimization. We take a further step towards improving the accuracy of the prediction model via the weighted combination of multiple regression methods. A proposed (GA)-based mixture optimization is proposed, building on the obtained multi-regression model.
  arXiv  Detail & Related papers  (2021-06-13T16:10:32Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [54.94763543386523]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)
• Transformer Hawkes Process [79.16290557505211]
  We propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies. THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
  arXiv  Detail & Related papers  (2020-02-21T13:48:13Z)

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.