Generalized Linear Models

Also known as GLM

Generalized linear models (GLMs) model outcomes such as binary responses and counts by linking a linear predictor to a distribution, as in logistic and Poisson regression. Common approaches include likelihood-based estimation, Lasso regularization for feature selection, mixed-effects models for grouped or longitudinal data, and online methods such as mirror descent for contextual bandits. Researchers also extend GLMs with neural components or latent structure, testing them on synthetic and real data in settings such as count prediction, transfer learning, and streaming estimation. A central challenge is balancing flexible, scalable models with interpretability, reliable uncertainty estimates, and guarantees when data are sparse, shifting, or misspecified.

Latest papers

25 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Discrete Score Matching Enables Causal Discovery from Count Data
Sep 30, 2026Euijong Song, Hyewon Park, Gunwoong ParkScore Matching Causal Discovery
Department of Statistics Seoul National University, Korea · Interdisciplinary Program in Artificial Intelligence; Institute for Data Innovation in Science, Seoul National University, Korea
Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models
Sep 21, 2026Alex Kipnis, Marcel Binz, Eric SchulzNeural Posterior Estimation Amortized Inference
Institute for Human-Centered AI, Helmholtz Munich, Germany
LiD-GLM: Lipschitz-constrained Deep Generalized Linear Models
Aug 17, 2026Tom Splittgerber, Niklas Koenen, Marvin N. Wright +1Interpretable ML Generalized Linear Models
Competence Center for Clinical Trials Bremen, University of Bremen · Leibniz Institute for Prevention Research and Epidemiology – BIPS & University of Bremen
Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data
Jul 6, 2026Yuankang Zhao, Youngsoo Baek, Felipe A. Medeiros +2Bayesian Inference Hierarchical Bayesian Modeling
Department of Biostatistics & Bioinformatics, Duke University · Department of Ophthalmology, University of Miami
Mixed-Precision Communication-Avoiding SGD for Generalized Linear Models on GPUs
Jun 16, 2026Aditya Devarakonda, Irene Simó Muñoz, Giulia GuidiGPU Acceleration Communication-Efficient Distributed Training
Department of Computer Science, Wake Forest University, Winston-Salem, NC, USA · Department of Computer Science, Cornell University, Ithaca, NY, USA
p-PSO: A Penalized Particle Swarm Optimization Technique for Finding D-Optimal Designs with Mixed Factors in Generalized Linear Models
Jun 14, 2026Shrabanti Chowdhury, Abhyuday MandalConstrained Optimization Particle Swarm Optimization
Icahn School of Medicine at Mount Sinai New York, NY 10029-5674 · University of Georgia Athens, GA 30602-7952
Renewable Lasso without Batch-Number Constraints: A Gradient-Enhanced Approach
Jun 10, 2026Junzhuo Gao, Ling Peng, Xu Guo +1Generalized Linear Models Streaming Algorithms
a Department of Mathematics, City University of Hong Kong, Hong Kong, China · e CityUHK Shenzhen Research Institute, Shenzhen, China[stat.ML] · b School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, China +2
Harnessing Source Heterogeneity for Cluster-Structured Transfer Learning
Jun 3, 2026Xiaohui Yin, Jun Jin, Shane J. Sacco +2Cross-Domain Transfer Learning Transfer Learning
Department of Statistics, University of Connecticut · Department of Public Health Sciences, Henry Ford Health · Center for Population Health, University of Connecticut Health Center
The Importance of Being Statistically Earnest: A Critical Re-evaluation of GSM-Symbolic
May 27, 2026Dominika Agnieszka Długosz, Arlindo Oliveira, Natalia Díaz-RodríguezLLM Evaluation Generalized Linear Models
Instituto Superior Técnico & INESC-ID, Universidade de Lisboa, Portugal · Dept. of Computer Science and AI & DaSCI Institute, Universidad de Granada, Spain
Nonstationary Generalized Linear Bandits with Discounted Online Mirror Descent
May 25, 2026Joongkyu Lee, Min-hwan OhMulti-Armed Bandits Dynamic Regret Minimization
Seoul National University
When Stronger Triggers Backfire: A High-Dimensional Theory of Backdoor Attacks
May 21, 2026Donald Flynn, Hadas Yaron Goldhirsh, Jonathan P. Keating +1Backdoor Attacks Statistical Learning Theory
1Mathematical Institute, University of Oxford · School of Mathematical Science, Tel Aviv University · School of Mathematical Science and Computer Science, Tel Aviv University
From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal $k$ -Sparse GLMs
May 21, 2026Jiachang Liu, Andrea LodiBranch and Bound GPU Kernel Optimization
∗Center for Data Science for Enterprise and Society, Cornell University, Ithaca, USA · †School of Operations Research and Information Engineering, Cornell University, Ithaca, USA · ‡Jacobs Technion-Cornell Institute, Cornell Tech and Technion–IIT, New York, USA
A renormalization-group inspired lattice-based framework for piecewise generalized linear models
May 6, 2026Joshua C. ChangInterpretable ML Statistical Learning Theory
NIH Clinical Center, Rehabilitation Medicine · Mederrata Research Inc · Sound Prediction Inc
Forecast Sports Outcomes under Efficient Market Hypothesis: Theoretical and Experimental Analysis of Odds-Only and Generalised Linear Models
Apr 19, 2026Kaito Goto, Naoya Takeishi, Takehisa YairiProbabilistic Forecasting Generalized Linear Models
Research Center for Advanced Science and Technology, The University of Tokyo, Japan
Neural Generalized Mixed-Effects Models
Apr 13, 2026Yuli Slavutsky, Sebastian Salazar, David M. BleiMixed-Effects Models Generalized Linear Models
Department of Statistics, Columbia University, New York, USA · Department of Computer Science, Columbia University, New York, USA
A Muon-Accelerated Algorithm for Low Separation Rank Tensor Generalized Linear Models
Apr 6, 2026Xiao Liang, Shuang LiGeneralized Linear Models Tensor Decomposition
Department of Electrical and Computer Engineering, Iowa State University, Ames, 50014, Iowa, USA
Understanding When Poisson Log-Normal Models Outperform Penalized Poisson Regression for Microbiome Count Data
Apr 4, 2026Daniel Agyapong, Julien Chiquet, Jane Marks +1Generalized Linear Models
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, U.S.A. · UMR MIA Paris-Saclay, Université Paris-Saclay, AgroParisTech, INRAE, Palaiseau, France · Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, U.S.A. +1
Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs
Mar 19, 2026Nathan Weill, Kaizheng WangUnsupervised Domain Adaptation Covariate Shift
Department of IEOR, Columbia University. · Department of IEOR and Data Science Institute, Columbia University.
Accelerating Single-Pass SGD for Generalized Linear Prediction
Mar 2, 2026Qian Chen, Shihong Ding, Cong FangStochastic Gradient Descent Momentum Methods
Peking University
GPU-friendly and Linearly Convergent First-order Methods for Certifying Optimal $k$ -sparse GLMs
Mar 1, 2026Jiachang Liu, Andrea Lodi, Soroosh ShafieeConstrained Optimization Generalized Linear Models
Center for Data Science for Enterprise and Society, Cornell University, Ithaca, USA · School of Operations Research and Information Engineering, Cornell University, Ithaca, USA · Jacobs Technion-Cornell Institute, Cornell Tech and Technion–IIT, New York, USA
A Jointly Efficient and Near-Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions
Feb 11, 2026Sanghwa Kim, Junghyun Lee, Se-Young YunMulti-Armed Bandits Online Mirror Descent
Kim Jaechul Graduate School of AI, KAIST Seoul 02455, Republic of Korea
A New Family of Poisson Non-negative Matrix Factorization Methods Using the Shifted Log Link
Jan 9, 2026Eric Weine, Peter Carbonetto, Rafael A. Irizarry +1Non-Negative Matrix Factorization Generalized Linear Models
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA · Department of Human Genetics, University of Chicago, Chicago, IL, USA · Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA +1
Basic Inequalities for First-Order Optimization with Applications to Statistical Risk Analysis
Dec 31, 2025Seunghoon Paik, Kangjie Zhou, Matus Telgarsky +1Early Stopping Gradient Descent
University of California, Berkeley · Columbia University · New York University
Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits
Oct 12, 2025Jiazheng Sun, Weixin Wang, Pan XuEnsemble Learning Contextual Bandits
Duke University
Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts
Jul 14, 2019Thin Nguyen-Van, Faicel Chamroukhi, Ha Hoang Van +1Mixture of Experts Sparse Mixture-of-Experts
Faculty of Mathematics and Computer Science, University of Science, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam · IRT SystemX, 91120 Palaiseau, France +1

Sep 30, 2026J/K move · Enter open · S save

Discrete Score Matching Enables Causal Discovery from Count Data

Euijong Song, Hyewon Park, Gunwoong Park

Department of Statistics Seoul National University, Korea · Interdisciplinary Program in Artificial Intelligence; Institute for Data Innovation in Science, Seoul National University, Korea

Score Matching Causal Discovery Causal Identifiability Generalized Linear Models Score-Based Causal Discovery

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Count data pose a challenge for score-matching-based causal discovery: derivatives are unavailable, and simply replacing them with finite differences does not generally suffice for causal discovery. We generalize SCORE's constant-curvature criterion (Rolland et al., 2022) by conditioning on the node's value, yielding the conditional curvature score (CCS) for ordering. We also extend curvature-based parent recovery through the off-diagonal curvature score (OCS), enabling directed acyclic graph (DAG) recovery with both scores constructed from score functions for continuous data and concrete scores for counts. In the bivariate setting, zero CCS exactly characterizes a semiparametric generalized linear model (GLM) conditional form in which the conditional family need not be specified in advance, unlike in classical GLMs. For bivariate semiparametric GLM DAGs under our regularity condition, canonical-parameter nonlinearity is necessary and sufficient for identifiability. In multivariate DAGs, this nonlinearity enables DAG recovery through CCS and OCS. Our framework identifies a new class of semiparametric GLM DAGs that strictly contains the nonlinear Gaussian ANM class identified by SCORE. We introduce DISCO (DIscrete SCOre), a count-DAG recovery algorithm that estimates CCS and OCS using discrete diffusion. Experiments demonstrate accurate DAG recovery across Poisson, negative binomial, binomial, and mixed-family settings, as well as scalability to 1,000-node DAGs on a single GPU.