Representer Point Selection for Explaining Regularized High-dimensional Models

• URL: http://arxiv.org/abs/2305.20002v2
• Date: Sat, 1 Jul 2023 00:13:29 GMT
• Title: Representer Point Selection for Explaining Regularized High-dimensional Models
• Authors: Che-Ping Tsai, Jiong Zhang, Eli Chien, Hsiang-Fu Yu, Cho-Jui Hsieh, Pradeep Ravikumar
• Abstract summary: We introduce a class of sample-based explanations we term high-dimensional representers. Our workhorse is a novel representer theorem for general regularized high-dimensional models. We study the empirical performance of our proposed methods on three real-world binary classification datasets and two recommender system datasets.
• Score: 105.75758452952357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model in terms of importance weights for each of the training samples. Our workhorse is a novel representer theorem for general regularized high-dimensional models, which decomposes the model prediction in terms of contributions from each of the training samples: with positive (negative) values corresponding to positive (negative) impact training samples to the model's prediction. We derive consequences for the canonical instances of $\ell_1$ regularized sparse models, and nuclear norm regularized low-rank models. As a case study, we further investigate the application of low-rank models in the context of collaborative filtering, where we instantiate high-dimensional representers for specific popular classes of models. Finally, we study the empirical performance of our proposed methods on three real-world binary classification datasets and two recommender system datasets. We also showcase the utility of high-dimensional representers in explaining model recommendations.

Related papers

• A Two-Phase Recall-and-Select Framework for Fast Model Selection [13.385915962994806]
  We propose a two-phase (coarse-recall and fine-selection) model selection framework. It aims to enhance the efficiency of selecting a robust model by leveraging the models' training performances on benchmark datasets. It has been demonstrated that the proposed methodology facilitates the selection of a high-performing model at a rate about 3x times faster than conventional baseline methods.
  arXiv  Detail & Related papers  (2024-03-28T14:44:44Z)
• A Two-Scale Complexity Measure for Deep Learning Models [2.7446241148152257]
  We introduce a novel capacity measure 2sED for statistical models based on the effective dimension. The new quantity provably bounds the generalization error under mild assumptions on the model. simulations on standard data sets and popular model architectures show that 2sED correlates well with the training error.
  arXiv  Detail & Related papers  (2024-01-17T12:50:50Z)
• Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers [66.36045164286854]
  We analyze a set of existing bias features and demonstrate there is no single model that works best for all the cases. By choosing an appropriate bias model, we can obtain a better robustness result than baselines with a more sophisticated model design.
  arXiv  Detail & Related papers  (2022-10-28T17:52:10Z)
• Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models [56.88106830869487]
  We introduce equi-tuning, a novel fine-tuning method that transforms (potentially non-equivariant) pretrained models into group equivariant models. We provide applications of equi-tuning on three different tasks: image classification, compositional generalization in language, and fairness in natural language generation.
  arXiv  Detail & Related papers  (2022-10-13T08:45:23Z)
• Generalization Properties of Retrieval-based Models [50.35325326050263]
  Retrieval-based machine learning methods have enjoyed success on a wide range of problems. Despite growing literature showcasing the promise of these models, the theoretical underpinning for such models remains underexplored. We present a formal treatment of retrieval-based models to characterize their generalization ability.
  arXiv  Detail & Related papers  (2022-10-06T00:33:01Z)
• On generative models as the basis for digital twins [0.0]
  A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account for the uncertainty present in most structural modelling applications.
  arXiv  Detail & Related papers  (2022-03-08T20:34:56Z)
• Training Experimentally Robust and Interpretable Binarized Regression Models Using Mixed-Integer Programming [3.179831861897336]
  We present a model-based approach to training robust and interpretable binarized regression models for multiclass classification tasks. Our MIP model balances the optimization of prediction margin and model size by using a weighted objective. We show the effectiveness of training robust and interpretable binarized regression models using MIP.
  arXiv  Detail & Related papers  (2021-12-01T11:53:08Z)
• PSD Representations for Effective Probability Models [117.35298398434628]
  We show that a recently proposed class of positive semi-definite (PSD) models for non-negative functions is particularly suited to this end. We characterize both approximation and generalization capabilities of PSD models, showing that they enjoy strong theoretical guarantees. Our results open the way to applications of PSD models to density estimation, decision theory and inference.
  arXiv  Detail & Related papers  (2021-06-30T15:13:39Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Predicting Multidimensional Data via Tensor Learning [0.0]
  We develop a model that retains the intrinsic multidimensional structure of the dataset. To estimate the model parameters, an Alternating Least Squares algorithm is developed. The proposed model is able to outperform benchmark models present in the forecasting literature.
  arXiv  Detail & Related papers  (2020-02-11T11:57:07Z)

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.