In this article we study the right-angled Artin subgroups of a given right-angled Artin group. Starting with a graph $\gam$, we produce a new graph through a purely combinatorial procedure, and call it the extension graph $\gam^e$ of $\gam$. We produce a second graph $\gam^e_k$, the clique graph of $\gam^e$, by adding …
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48 results for “GAM”
New research finds tree-based GAMs are most trustworthy and fair.
problem Variability in GAM algorithms leads to inconsistent models.
method Quantitative and qualitative analysis of various GAM algorithms.
result Tree-based GAMs are the most trustworthy and fair.
GAM(L)A model improves interpretability of machine learning models.
problem Interpretable machine learning models for high predictive performance.
method Combines partial linear models with variable selection for accurate prediction and interpretability.
result GAM(L)A outperforms parametric models and is comparable to black-box models like random forest and gradient boosting.
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.
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…
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…
GAMformer bridges tabular models and interpretability, offering a single-pass approach.
problem Lack of interpretability in tabular foundation models like TabPFN.
method In-context learning for GAM shape functions, training on synthetic data.
result GAMformer performs comparably to other leading GAMs across various classification benchmarks.
Regularizes GAMs to improve interpretability by reducing concurvity.
problem Susceptibility of GAMs to concurvity reduces interpretability.
method Proposes a regularizer to penalize pairwise correlations of non-linearly transformed features.
result Improves interpretability and reduces concurvity without sacrificing prediction quality.
For every orientable surface of finite negative Euler characteristic, we find a right-angled Artin group of cohomological dimension two which does not embed into the associated mapping class group. For a right-angled Artin group on a graph $\gam$ to embed into the mapping class group of a surface , we show that the …
RAMs improve GAMs' accuracy by fitting components to subregions of feature space.
problem Subpar accuracy in GAMs due to inability to capture feature interactions.
method Identify subregions of feature space where interactions are minimized, fitting one component per subregion.
result RAMs offer improved expressiveness compared to GAMs while maintaining interpretability.
Generalized additive models (GAMs) are a widely used class of models of interest to statisticians as they provide a flexible way to design interpretable models of data beyond linear models. We here propose a scalable and well-calibrated Bayesian treatment of GAMs using Gaussian processes (GPs) and leveraging recent adv…
Comparison of generalised additive models and neural networks in applications: A systematic reviewstat.ML
This review compares GAMs and neural networks on real-world tabular data.
problem Comparing the performance and characteristics of GAMs and neural networks in tabular data applications.
method Systematic review following PRISMA guidelines, extracting and analysing key attributes from 143 papers and 430 datasets.
result No consistent evidence of superiority for either GAMs or neural networks, with performance trade-offs depending on dataset characteristics.
Standard autoregressive seq2seq models are easily trained by max-likelihood, but tend to show poor results under small-data conditions. We introduce a class of seq2seq models, GAMs (Global Autoregressive Models), which combine an autoregressive component with a log-linear component, allowing the use of global \textit{a…
A barrier to the wider adoption of neural networks is their lack of interpretability. While local explanation methods exist for one prediction, most global attributions still reduce neural network decisions to a single set of features. In response, we present an approach for generating global attributions called GAM, w…
Hedonic models predict 84-92% of U.S. real estate prices, highlighting environmental factors' impact.
problem Predicting real estate prices using hedonic models with environmental factors.
method P-spline generalized additive models for real estate prices, contrasting with linear and polynomial models.
result GAM models explain 84-92% of U.S. real estate price variance, with environmental factors contributing minimally.
Improved portfolio optimization using GAM factor models.
problem Enhancing CVaR portfolio optimization performance.
method Combines autoregressive filters with factor regressions to predict stock returns.
result Substantial improvement in portfolio performances with GAM models.
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.
Instantly interprets black-box models using additive models.
problem Efficiency and representation power of SHAP explanations.
method Variational perspective linking GAM models and SHAP explanations; InstaSHAP method.
result Automatic computation of Shapley values in a single forward pass.
Multiple generalized additive models (GAMs) are a type of distributional regression wherein parameters of probability distributions depend on predictors through smooth functions, with selection of the degree of smoothness via regularization. Multiple GAMs allow finer statistical inference by incorporating explana…
New model captures complex relationships from experimental data.
problem Capturing intricate feature interactions in empirical data.
method Shape Arithmetic Expressions (SHAREs) combining GAMs and mathematical expressions.
result SHAREs model captures complex feature interactions.
Analyzing mobility behavior of users is extremely useful to create or improve existing services. Several research works have been done in order to study mobility behavior of users that mainly use users' significant locations. However, these existing analysis are extremely intrusive because they require the knowledge of…
Bayesian GAMs improve predictive performance for high-dimensional data.
problem Sparse regularization in GAMs leads to excess shrinkage and difficulty in selecting nonlinear effects.
method Developed a novel spike-and-slab LASSO prior and scalable EM-Coordinate Descent algorithm.
result Improved predictive and computational performance compared to existing models.
Study integrates ESG factors into home price predictions for U.S. cities.
problem Predicting average annual home prices using ESG factors.
method Used P-spline GAM and GLM models, transformed time series data.
result ESG factors influence home prices differently by city.
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.
ParamBoost uses gradient boosting to create interpretable non-linear models with constraints.
problem Creating interpretable non-linear models with expert knowledge constraints.
method Gradient Boosting of cubic polynomials with specified constraints.
result ParamBoost outperforms state-of-the-art GAMs in real-world datasets.
Global Autoregressive Models (GAMs) are a recent proposal [Parshakova et al., CoNLL 2019] for exploiting global properties of sequences for data-efficient learning of seq2seq models. In the first phase of training, an Energy-Based model (EBM) over sequences is derived. This EBM has high representational power, but is u…
SurvNAM explains survival model predictions using machine learning.
problem Explaining predictions of black-box survival models.
method SurvNAM is a modified Neural Additive Model (NAM) trained with a specific loss function based on Cox model and GAM.
result SurvNAM allows local and global explanation of survival model predictions.
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. …
We derive a new radial link for binary classification under shared elliptical distributions.
problem Binary classification under shared-generator elliptical class-conditional distributions.
method We derive the Bayes radial-link family from the within-class radius law and estimate it by a finite fractional-power stochastic-polynomial projection.
result The derived link is asymptotically Bayes-optimal and significantly better than QDA on various benchmarks.
Statistical Learnability of Generalized Additive Models based on Total Variation Regularizationstat.ML
A generalized additive model (GAM, Hastie and Tibshirani (1987)) is a nonparametric model by the sum of univariate functions with respect to each explanatory variable, i.e., , where is -th component of a sample . In this paper, we introd…
LLMs excel at summarizing and repairing complex models without needing full models.
problem Understanding and repairing complex models like GAMs.
method Hierarchical reasoning and extensive background knowledge.
result LLMs can detect anomalies, describe reasons, and suggest repairs.
High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networkscs.LG
Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.
problem Predicting high-resolution peak demand from limited lower-resolution data.
method Combines generalized additive models (GAM) and deep neural networks (DNN) for half-hourly load forecasting.
result Proposed method reduces out-of-sample RMSE by 57.4% compared to benchmark.
Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in co…
Model uses GAMs to forecast hourly electricity load weeks to one year ahead.
problem Accurate mid-term hourly load forecasting for power plant operation and energy management.
method Generalized Additive Models (GAMs) with P-splines and autoregressive post-processing.
result Significantly enhanced forecasting accuracy compared to state-of-the-art methods.
KAPLAN-HR models survival data without manual interactions, outperforming existing methods.
problem Survival analysis challenges with complex covariates and time-varying effects.
method Kolmogorov-Arnold Networks (KAN) for nonparametric hazard estimation.
result KAPLAN-HR matches or exceeds existing methods in clinical survival data.
The monotonicity of the Franz-Parisi potential is equivalent with Low-degree MMSE lower boundsmath.ST
New work shows FP potential monotonicity equals low-degree polynomial estimators limits.
problem Establishing a precise mathematical relationship between statistical physics and polynomial estimators limits.
method Analyzing Gaussian additive models (GAMs) to show FP potential monotonicity equals low-degree polynomial estimators limits.
result For a broad family of Gaussian additive models, the power of low-degree polynomials is equivalent to the monotonicity of the annealed FP potential.
Robust model detects outliers in spatiotemporal epidemic data.
problem Outliers in epidemic data can mislead public health decisions.
method RST-GAM with mean-shift, adaptive Lasso, splines, and proximal algorithm.
result Demonstrates effectiveness in real-world COVID-19 data analysis.
Distributional Fitting and Tail Analysis of Lead-Time Compositions: Nights vs. Revenue on Airbnbq-fin.ST
Study analyzes Airbnb lead-time distributions for Nights Booked and Gross Booking Value, finding divergent shapes and tail behavior.
problem Analyzing lead-time distributions for Airbnb demand metrics.
method Compositional analysis of daily lead-time vectors, fitting Gamma, Weibull, and Lognormal distributions, using generalized Pareto for tail inference.
result Lead-time distributions for Nights Booked and Gross Booking Value diverge, with GBV concentrating more in mid-range horizons.
mcanalysis quantifies menstrual cycle effects in health data.
problem Lack of standardised statistical methods for menstrual cycle research.
method Fourier-basis generalised additive model (GAM) pipeline.
result Nine out of 15 health outcomes showed significant association with menstrual cycle.
Droughts, with their increasing frequency of occurrence, continue to negatively affect livelihoods and elements at risk. For example, the 2011 in drought in east Africa has caused massive losses document to have cost the Kenyan economy over $12bn. With the foregoing, the demand for ex-ante drought monitoring systems is…
Climate has been an important factor in shaping the distribution and incidence of dengue cases in tropical and subtropical countries. In Costa Rica, a tropical country with distinctive micro-climates, dengue has been endemic since its introduction in 1993, inflicting substantial economic, social, and public health repe…
Modeling volatility with Chained Gamma Distributions for financial time series.
problem Volatility clustering in financial time series, especially in estimating temporal autocorrelation of logarithmic variance of returns.
method Dynamic Bayesian Network with conjugate prior relation of normal-gamma and gamma-gamma, using variational methods for quick approximate solutions.
result The model can express heavier tails than Gaussians, achieving positive excess kurtosis, and runs faster than Monte Carlo methods.
Study predicts stream turbidity using surrogate data and meta-model.
problem Costly turbidity sensor deployment limits monitoring networks.
method Dynamic regression (ARIMA), LSTM, GAM models; surrogate covariates (rainfall, water level, temperature, solar exposure); meta-model combining strengths of individual models.
result ARIMA and GAM models with all covariates outperform single models; meta-model yields highest accuracy.
Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year. Since the industry is currently far from being endowed with sufficient computational capacities to fully simulate these distributions, the insurers have t…
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.
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.
EBM improves car insurance claim severity and frequency prediction while maintaining interpretability.
problem Balancing predictive accuracy and interpretability in insurance claim modeling.
method Combines GAM and cyclic gradient boosting, providing interpretable predictions.
result EBM outperforms benchmark models in claim severity and frequency prediction.
Surrogate-based analysis of interactions via local effect smooths
problem Detecting and characterizing feature interactions in machine learning models
method Surrogate-based analysis using generalized additive models
result Empirical validation of effectiveness for pairwise interactions