We introduce GAMSEL (Generalized Additive Model Selection), a penalized likelihood approach for fitting sparse generalized additive models in high dimension. Our method interpolates between null, linear and additive models by allowing the effect of each variable to be estimated as being either zero, linear, or a low-co…
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48 results for “Generalized Additive Models”
Researchers review challenges in interpreting additive models, especially neural additive models.
problem Challenges in interpreting additive models, particularly neural additive models.
method Review of generalized additive models and discussion of nonidentifiability.
result Challenges in claiming interpretability or suitability for safety-critical applications of additive models.
Additive Gaussian Processesstat.ML
We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-expo…
Many methods to explain black-box models, whether local or global, are additive. In this paper, we study global additive explanations for non-additive models, focusing on four explanation methods: partial dependence, Shapley explanations adapted to a global setting, distilled additive explanations, and gradient-based e…
New models improve machine learning accuracy and transparency in finance.
problem Black-box machine learning models lack interpretability in regulated industries.
method Introducing generalized groves of neural additive models with clear feature categories and interactions.
result Generalized groves of neural additive models achieve high accuracy with predominantly linear and sparse nonlinear components.
Sparse generalized additive models (GAMs) are an extension of sparse generalized linear models which allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. …
Proposes a new model for high-dimensional data analysis with unknown link function.
problem Estimating link function, component functions, and variable interactions in high-dimensional data.
method Generalized Sparse Additive Model with Unknown Link Function (GSAMUL) using B-spline basis and MLP network for link estimation, with -norm regularizer for variable selection.
result Can realize both variable selection and hidden interaction.
Boosted additive models reveal new insights and potential pathologies.
problem Theoretical understanding of boosted additive models (BAMs) and their convergence behavior.
method Study of solution paths of BAMs and derivation of convergence results.
result Uncovering pathologies of boosting for certain additive model classes.
Learning rates for the risk of kernel based quantile regression estimators in additive modelsstat.ML
Additive models play an important role in semiparametric statistics. This paper gives learning rates for regularized kernel based methods for additive models. These learning rates compare favourably in particular in high dimensions to recent results on optimal learning rates for purely nonparametric regularized kernel …
BARMPy offers a Python package for Bayesian Additive Regression Models.
problem Making complex Bayesian models accessible to machine learning practitioners.
method Object-oriented design compatible with SciKit-Learn, documentation and tutorial provided.
result Ease of use and compatibility with existing machine learning tools.
Study on signal detection in sparse additive models with nonasymptotic minimax rates.
problem Signal detection in sparse additive models.
method Nonasymptotic minimax analysis of signal detection in sparse additive models.
result Established minimax separation rate for signal detection.
AutoML improves electricity demand forecasting models.
problem Optimizing GAM and state-space model parameters for short-term forecasting.
method Automated online generalized additive model selection using DRAGON package.
result The approach enhances predictive performance of adaptive models.
We propose a new sparsity-smoothness penalty for high-dimensional generalized additive models. The combination of sparsity and smoothness is crucial for mathematical theory as well as performance for finite-sample data. We present a computationally efficient algorithm, with provable numerical convergence properties, fo…
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processesstat.ML
New model estimates higher-order interactions in stochastic processes using lower-dimensional projections.
problem Estimating higher-order interaction effects in stochastic processes with limited data.
method Additive Poisson Process (APP) combines information geometry and generalized additive models to model intensity functions in lower dimensions.
result The model can estimate higher-order intensity functions with sparse data.
The study reveals decision trees' limitations in fitting data from additive models, proving a generalization lower bound.
problem Understanding the generalization performance of decision trees on additive models.
method Analyzing decision tree algorithms with sparse additive models, proving generalization lower bounds.
result Generalization lower bounds for decision trees on sparse additive models are much worse than minimax rates.
Combines additivity and active subspaces for high-dimensional Gaussian process modeling.
problem High-dimensional Gaussian process modeling challenges due to the curse of dimensionality.
method Combines additivity and active subspaces with a multi-fidelity strategy.
result Shows advantages through experiments on synthetic functions and datasets.
The paper tackles high-dimensional Bayesian optimization using tree-structured additive models.
problem Scaling Bayesian Optimization to high-dimensional problems.
method Tree-structured additive models with hybrid graph learning and zooming-based algorithms.
result Demonstrates faster model learning and reduced model complexity in high-dimensional settings.
DAEs can generate images without additional loss terms, inheriting VAE properties.
problem Difficulty in using VAEs for practical generative modelling.
method Empirical exploration of DAEs for image generation without novel methods.
result DAEs can generate images successfully without additional loss terms.
We provide a unified view of additive explanations for dependent inputs.
problem Challenges in obtaining a tractable representation and estimating the decomposition for dependent inputs.
method Combining Hilbert space methods with generalized functional ANOVA, we build an explicit decomposition Riesz Basis.
result Proposed a simple yet powerful algorithm to estimate the decomposition from data.
The paper explores risk-minimization for exponential additive models, providing mathematical expressions and numerical examples.
problem Risk-minimization in incomplete markets for exponential additive models.
method Derive explicit mathematical expressions for local risk-minimization strategies in exponential additive models.
result Provide necessary conditions for deriving expressions and confirm integrability conditions for specific models.
Generalized additive models (GAMs) are favored in many regression and binary classification problems because they are able to fit complex, nonlinear functions while still remaining interpretable. In the first part of this paper, we generalize a state-of-the-art GAM learning algorithm based on boosted trees to the multi…
G-SHAP generates multiple types of explanations for machine learning models.
problem Understanding model predictions and their differences across groups.
method Generalization of SHAP method to produce additional types of explanations.
result G-SHAP produces explanations for classification, intergroup differences, and model failure.
We study additive models built with trend filtering, i.e., additive models whose components are each regularized by the (discrete) total variation of their th (discrete) derivative, for a chosen integer . This results in th degree piecewise polynomial components, (e.g., gives piecewise constant co…
We analyze a family of methods for statistical causal inference from sample under the so-called Additive Noise Model. While most work on the subject has concentrated on establishing the soundness of the Additive Noise Model, the statistical consistency of the resulting inference methods has received little attention. W…
We consider the problem of sparse variable selection in nonparametric additive models, with the prior knowledge of the structure among the covariates to encourage those variables within a group to be selected jointly. Previous works either study the group sparsity in the parametric setting (e.g., group lasso), or addre…
SFG improves on-manifold sampling without labels or additional training.
problem Guiding score-based models on manifolds without labeled data or extra training.
method Developed saddle-free guidance (SFG) that uses curvature of log density estimates.
result SFG achieves state-of-the-art metrics in image generation without labeled data or additional training.
Theoretical analysis explains why models generalize after overfitting in modular addition.
problem Understanding why models generalize after overfitting in modular addition.
method Theoretical analysis and gradient descent behavior of two-layer quadratic networks and Transformers.
result Two-layer quadratic networks and simple Transformers generalize well after initially overfitting, indicating grokking.
neuralGAM package interprets neural networks by fitting them to feature contributions.
problem difficulty understanding neural network decisions
method Generalized Additive Neural Networks (GAM)
result interpretable Deep Learning model with accurate feature contributions
Unified approach for interpretable regression with flexible modeling.
problem Combining predictive adaptivity with interpretability in heterogeneous data.
method Combining random Fourier features, spectral feature map, principal component analysis, Gaussian mixture model, and cluster-specific generalized additive models.
result Consistently improves upon classical and black-box models across benchmark datasets.
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channelsstat.ML
The paper analyzes generalization of noisy iterative algorithms using communication theory.
problem Generalization of models trained by noisy iterative algorithms under different distributions.
method Connecting noisy iterative algorithms to additive noise channels in communication theory.
result Distribution-dependent generalization bounds for noisy iterative algorithms.
A method interprets black-box models using an ensemble of gradient boosting machines.
problem Local and global interpretation of black-box models.
method An ensemble of gradient boosting machines (GBMs) to form a generalized additive model.
result Efficiency and properties demonstrated on synthetic and real datasets.
A new method for analyzing shapes and forms using additive models on manifolds.
problem Analyzing shapes and forms under geometric transformations.
method Extending generalized additive regression to models for shapes/forms using squared geodesic distance and Riemannian -Boosting algorithm.
result Automated model selection and intuitive visualization of covariate effects in shape/form space.
The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator…
Neural model improves option pricing by calibrating additive process term structure.
problem Calibrating additive process models for option pricing with time-dependent parameters.
method Proposes neural term structure model using feedforward neural networks to represent term structure.
result Improves option pricing accuracy with neural term structure model.
TSL learns separable models to avoid signal cancellation and off-support extrapolation.
problem Signal cancellation and off-support extrapolation in additive models.
method Tensor Separation Learning (TSL) via stagewise greedy procedure with orthogonal refitting.
result TSL avoids information loss caused by marginalizing higher-order interactions.
Paper trains a Transformer to add numbers of any length.
problem Training Transformers to handle arbitrary-length addition.
method Autoregressive generation from right to left.
result Trains a Transformer to generalize addition of numbers of any length.
Fast classification for sparse models, even with correlated features.
problem Sparse classification with many correlated features.
method Linear and quadratic surrogate cuts, priority queue, and analytical solution for exponential loss.
result 2 to 5 times faster than previous approaches, interpretable models with comparable accuracy.
Proposes a new model for explainable recommendation systems.
problem Developing predictive models that provide explanations for item recommendations.
method Generalized Additive Models with Manifest and Latent Interactions (GAMMLI).
result Advantages in both predictive performance and explainability.
Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic data. We introduce a novel GAN with Autoencoder (GAN-AE) architecture to generate synthetic samples for variable length, multi-feature sequence datasets. In this model, we develop a GAN architecture with…
Meta Additive Model learns auto-weighting for robust sparse learning.
problem Sparse models' sensitivity to atypical data and noise.
method Bilevel optimization framework with meta data-driven weighting.
result MAM outperforms state-of-the-art models under various data corruptions.
Proposes a new video attack method that multiplies perturbation to improve model robustness.
problem Challenges existing defense methods for video recognition models against additive adversarial attacks.
method Introduces Multiplicative Adversarial Videos (MultAV) to impose perturbation by multiplication.
result Model trained against additive attacks is less robust to MultAV.
ProtoNAM models tabular data with neural networks, making predictions transparent.
problem Tabular data analysis using neural networks lacks transparency and accuracy compared to tree-based methods.
method ProtoNAM introduces prototypes into neural networks to model tabular data while maintaining explainability.
result ProtoNAM outperforms existing NN-based GAMs and provides insights into learned feature patterns.
Logistic regression (LR) is widely used in clinical prediction because it is simple to deploy and easy to interpret. Nevertheless, being a linear model, LR has limited expressive capability and often has unsatisfactory performance. Generalized additive models (GAMs) extend the linear model with transformations of input…
We investigate a generalized stochastic model with the property known as mean reversion, that is, the tendency to relax towards a historical reference level. Besides this property, the dynamics is driven by multiplicative and additive Wiener processes. While the former is modulated by the internal behavior of the syste…
Paper introduces GAMs for interpretable learning-to-rank models.
problem Need for transparent ranking models in legal or policy scenarios.
method Developed generalized additive models (GAMs) for ranking tasks using neural networks.
result Neural ranking GAMs achieve better performance than traditional GAMs while maintaining interpretability.
Neural Additive Models combine neural nets with interpretable models for high stakes tasks.
problem Inability to understand how neural networks make decisions.
method Combines neural nets with generalized additive models to create Neural Additive Models (NAMs).
result NAMs are more accurate than intelligible models and as accurate as state-of-the-art generalized additive models.
A new class of risk measures called cash sub-additive risk measures is introduced to assess the risk of future financial, nonfinancial and insurance positions. The debated cash additive axiom is relaxed into the cash sub additive axiom to preserve the original difference between the numeraire of the current reserve amo…
The generalized partially linear additive model (GPLAM) is a flexible and interpretable approach to building predictive models. It combines features in an additive manner, allowing each to have either a linear or nonlinear effect on the response. However, the choice of which features to treat as linear or nonlinear is …