A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper introduces fair GLMs with convex penalty for equalizing GLM outcomes.
The generalized linear model (GLM) plays a key role in regression analyses. In high-dimensional data, the sparse GLM has been used but it is not robust against outliers. Recently, the robust methods have been proposed for the specific example of the sparse GLM. Among them, we focus on the robust and sparse linear regre…
New algorithm learns sparse GLMs for binary outcomes efficiently.
Generalized principal component analysis (GLM-PCA) facilitates dimension reduction of non-normally distributed data. We provide a detailed derivation of GLM-PCA with a focus on optimization. We also demonstrate how to incorporate covariates, and suggest post-processing transformations to improve interpretability of lat…
Paper analyzes sparse aggregation in GLMs with Kullback-Leibler risk bounds.
We study two randomized algorithms for generalized linear bandits. The first, GLM-TSL, samples a generalized linear model (GLM) from the Laplace approximation to the posterior distribution. The second, GLM-FPL, fits a GLM to a randomly perturbed history of past rewards. We analyze both algorithms and derive $\tilde{O}(…
A novel multi-objective optimization framework improves insurance pricing fairness.
DP-GD achieves dimension-independent convergence for unconstrained private GLMs.
Paper proposes an alternative to MLE for GLMs with non-canonical link functions.
New methods for quantifying insurance claim cost uncertainty using LightGBM and GLMs.
New algorithms for private GLM estimation with minimax lower bounds.
Paper analyzes GLM-tron for high-dimensional ReLU regression, providing upper and lower bounds.
A new method connects GLM and MLE for neuroimaging analysis.
Optimal downsampling improves GLM performance in imbalanced classification.
We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLM), a new method of nonparametric regression that accommodates continuous and categorical inputs, and responses that can be modeled by a generalized linear model. We prove conditions for the asymptotic unbiasedness of the DP-GLM regression mean fu…
Over the years, ensemble methods have become a staple of machine learning. Similarly, generalized linear models (GLMs) have become very popular for a wide variety of statistical inference tasks. The former have been shown to enhance out- of-sample predictive power and the latter possess easy interpretability. Recently,…
New tensor model reduces GLM estimation error and sample complexity.
Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome. Generalized linear models (GLMs) offer a particularly interpretable framework for such an analysis. In these high-dimensional problems, the number of covaria…
We study the application of dynamic pricing to insurance. We view this as an online revenue management problem where the insurance company looks to set prices to optimize the long-run revenue from selling a new insurance product. We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive …
Accurate statistical models of neural spike responses can characterize the information carried by neural populations. But the limited samples of spike counts during recording usually result in model overfitting. Besides, current models assume spike counts to be Poisson-distributed, which ignores the fact that many neur…
Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide powerful generalizations of linear regression, where the target variable is assumed to be a (possibly unknown) 1-dimensional function of a linear predictor. In general, these problems entail non-convex estimation procedures, and, in practice, itera…
Generalized linear models (GLMs) -- such as logistic regression, Poisson regression, and robust regression -- provide interpretable models for diverse data types. Probabilistic approaches, particularly Bayesian ones, allow coherent estimates of uncertainty, incorporation of prior information, and sharing of power acros…
Genomic models learn DNA sequences to predict functions.
A framework connects VAEs to GLMs for better model initialization and performance.
Develops a new GLM framework for claims reserving with adaptive estimation.
Extends matrix factorization for deviance-based losses with GLM theory.
In this paper, we study the problem of estimating smooth Generalized Linear Models (GLMs) in the Non-interactive Local Differential Privacy (NLDP) model. Different from its classical setting, our model allows the server to access some additional public but unlabeled data. In the first part of the paper we focus on GLMs…
Analyzes SGD dynamics in high-dimensional settings for GLMs and multi-index models.
New method controls FDR for sparse GLMs, identifying positive and negative relationships.
Improved robust regression algorithms with faster runtime and better estimation rates.
PANDA augments data to regularize GLM estimation and inference.
New method for GLMs under DP provides private uncertainty quantification.
Neuroscientific theory suggests that dopaminergic neurons broadcast global reward prediction errors to large areas of the brain influencing the synaptic plasticity of the neurons in those regions. We build on this theory to propose a multi-agent learning framework with spiking neurons in the generalized linear model (G…
The balance property is crucial for insurance pricing, ensuring total actuarial price equals loss. Maximum likelihood GLMs fulfill it, but Lindholm-Wüthrich suggests three methods, with constrained GLM being superior.
Paper develops methods for estimating GLMs and SNR under proportional asymptotics.
New method estimates GGLM parameters, overcoming non-convexity.
We rigorously prove statistical physics predictions for non-convex GLMs in high dimensions.
We consider the problem of efficiently computing the maximum likelihood estimator in Generalized Linear Models (GLMs) when the number of observations is much larger than the number of coefficients (). In this regime, optimization algorithms can immensely benefit from approximate second order information.…
Holistic GLMs add constraints for better model quality.
This paper introduces a novel online inference method for high-dimensional GLMs.
Generalized Linear Models (GLM) form a wide class of regression and classification models, where prediction is a function of a linear combination of the input variables. For statistical inference in high dimension, sparsity inducing regularizations have proven to be useful while offering statistical guarantees. However…
New method simplifies Bayesian analysis for categorical data.
Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes. In this paper, the problem of training a two-layer SNN is studied for the purpose of classification, under a Ge…
Study improves model fit by transferring info from related datasets.
We consider the recently proposed reinforcement learning (RL) framework of Contextual Markov Decision Processes (CMDP), where the agent interacts with a (potentially adversarial) sequence of episodic tabular MDPs. In addition, a context vector determining the MDP parameters is available to the agent at the start of eac…
Automates finding interactions in GLMs using neural networks.
BELIEF framework interprets GLMs using binary linear models.
Improved tensor GLM estimation for complex data.