Generalized Linear Model

Generalized linear models (GLMs) are a flexible class of statistical models extending linear regression to handle various response types, aiming to model the relationship between predictor variables and a response variable. Current research focuses on improving GLM efficiency and robustness through techniques like adaptive stochastic gradient descent, optimized confidence sequences, and novel algorithms for handling high-dimensional data, missing data, and adversarial corruptions. These advancements are impacting diverse fields, including healthcare (fairness in predictions), neuroscience (analyzing neural connectivity), and online recommendation systems (improving user experience), by enabling more accurate and reliable modeling of complex data.

Papers

June 9, 2022

Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models
Pranjal Awasthi, Abhimanyu Das, Weihao Kong, Rajat Sen
Robust Learning Generalized Linear Model Adversarial Corruption Label Corruption Gaussian Approximation General Linear Corruption Process

June 4, 2022

Combinatorial Causal Bandits
Shi Feng, Wei Chen
Causal Inference Causal Structure Generalized Linear Model Causal Bandit

May 31, 2022

Easy Variational Inference for Categorical Models via an Independent Binary Approximation
Michael T. Wojnowicz, Shuchin Aeron, Eric L. Miller, Michael C. Hughes
Variational Inference Generalized Linear Model Tractable Probabilistic Model Likelihood Estimation Binary Model Independent Model

May 30, 2022

Holistic Generalized Linear Models
Benjamin Schwendinger, Florian Schwendinger, Laura Vana
Generalized Linear Model Sparsity Constraint Task Constraint Linear Constraint

April 18, 2022

Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models
Yinan Li, Fang Liu
Scientific Inference Generalized Linear Model Regularization Effect Group Lasso Adaptive Lasso Noise Robust Loss

March 25, 2022

$p$-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets
Alexander Munteanu, Simon Omlor, Christian Peters
Generalized Linear Model Normal Distribution Tobit Model

February 23, 2022

Fast Sparse Classification for Generalized Linear and Additive Models
Jiachang Liu, Chudi Zhong, Margo Seltzer, Cynthia Rudin
Many Sparse Interpretable Model Generalized Linear Model Additive Model Fast Classification Sparse Classification Sparse Logistic Regression

January 31, 2022

Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension
Bruno Loureiro, Cédric Gerbelot, Maria Refinetti, Gabriele Sicuro, Florent Krzakala
LeArning Abstract High Dimensional Regularization Model High Dimension Convex Loss Robust Generalization Generalized Linear Model Fluctuation Analysis

January 7, 2022

Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships
Eunice Jun, Audrey Seo, Jeffrey Heer, René Just
Stronger Generalizability Statistical Model Generalized Linear Model Mixed Effect Model Formal Logic Conceptual Analysis

December 8, 2021

Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing
Ramji Venkataramanan, Kevin Kögler, Marco Mondelli
Estimation Task Generalized Linear Model Spectral Energy Distribution Approximate Message Invariant Subspace Signal Estimation

November 17, 2021

A Generalized Proportionate-Type Normalized Subband Adaptive Filter
Kuan-Lin Chen, Ching-Hua Lee, Bhaskar D. Rao, Harinath Garudadri
Sparsity Increase Generalized Linear Model Adaptive Filter Sparse Target Sparsity Penalty

Generalized Linear Model

Papers

Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models

Combinatorial Causal Bandits

Easy Variational Inference for Categorical Models via an Independent Binary Approximation

Holistic Generalized Linear Models

Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models

$p$-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets

Fast Sparse Classification for Generalized Linear and Additive Models

Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension

Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships

Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing

A Generalized Proportionate-Type Normalized Subband Adaptive Filter