Cover Image

MM-algorithms for traditional and convex NMF with Tweedie and Negative Binomial cost functions and empirical evaluation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	James, Elisabeth Sommer, Hobolth, Asger, Pelizzola, Marta
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On the Relation Between Autoencoders and Non-negative Matrix Factorization, and Their Application for Mutational Signature Extraction
by: Egendal, Ida, et al.
Published: (2024)

Identification of NMF by choosing maximum-volume basis vectors
by: Qi, Qianqian, et al.
Published: (2026)

Empirical Bayes Estimation with Side Information: A Nonparametric Integrative Tweedie Approach
by: Luo, Jiajun, et al.
Published: (2023)

Integrating opportunities and parametrized signatures for improved mutational processes estimation in extended sequence contexts
by: Laursen, Ragnhild, et al.
Published: (2026)

Distribution-free inference for LightGBM and GLM with Tweedie loss
by: Manna, Alokesh, et al.
Published: (2025)

Binomial flows: Denoising and flow matching for discrete ordinal data
by: Shenfeld, Yair, et al.
Published: (2026)

Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm
by: Zgodic, Anja, et al.
Published: (2023)

Rethinking the generalization of drug target affinity prediction algorithms via similarity aware evaluation
by: Zhang, Chenbin, et al.
Published: (2025)

Denoising data using convex relaxations
by: Fefferman, Charles, et al.
Published: (2026)

Combining Priors with Experience: Confidence Calibration Based on Binomial Process Modeling
by: Dong, Jinzong, et al.
Published: (2024)

The data augmentation algorithm
by: Roy, Vivekananda, et al.
Published: (2024)

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms
by: Petersen, Anne Helby
Published: (2024)

A cost-sensitive constrained Lasso
by: Blanquero, Rafael, et al.
Published: (2024)

Neyman-Pearson multiclass classification under label noise via empirical likelihood
by: Zhang, Qiong, et al.
Published: (2026)

A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
by: Kim, Youngseok, et al.
Published: (2022)

Asset price movement prediction using empirical mode decomposition and Gaussian mixture models
by: Palma, Gabriel R., et al.
Published: (2025)

Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem
by: Favaro, Stefano, et al.
Published: (2024)

Tight Generalization Error Bounds for Stochastic Gradient Descent in Non-convex Learning
by: Xiong, Wenjun, et al.
Published: (2025)

An empirical comparison of some outlier detection methods with longitudinal data
by: D'Orazio, Marcello
Published: (2025)

Gradient-based optimization for variational empirical Bayes multiple regression
by: Banerjee, Saikat, et al.
Published: (2024)

Multi-task learning via robust regularized clustering with non-convex group penalties
by: Okazaki, Akira, et al.
Published: (2024)

Neural Backward Filtering Forward Guiding
by: Yang, Gefan, et al.
Published: (2026)

Wild Bootstrap Inference for Non-Negative Matrix Factorization with Random Effects
by: Satoh, Kenichi
Published: (2026)

Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling
by: Chang, Jinyuan, et al.
Published: (2024)

Variational empirical Bayes variable selection in high-dimensional logistic regression
by: Tang, Yiqi, et al.
Published: (2025)

New insights into Elo algorithm for practitioners and statisticians
by: Szczecinski, Leszek
Published: (2026)

Classification from Positive and Biased Negative Data with Skewed Labeled Posterior Probability
by: Watanabe, Shotaro, et al.
Published: (2022)

Distributionally robust risk evaluation with an isotonic constraint
by: Gui, Yu, et al.
Published: (2024)

Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens
by: Lakkaraju, Kausik, et al.
Published: (2024)

Generation of Multivariate Discrete Data with Generalized Poisson, Negative Binomial and Binomial Marginal Distributions
by: Kwong, Chak, et al.
Published: (2026)

Targeted Sequential Indirect Experiment Design
by: Ailer, Elisabeth, et al.
Published: (2024)

Optimal convex $M$-estimation via score matching
by: Feng, Oliver Y., et al.
Published: (2024)

Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications
by: Sauer, Christina, et al.
Published: (2024)

FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimality
by: Li, Sai, et al.
Published: (2024)

Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality
by: Jin, Ying, et al.
Published: (2022)

Trunc-Opt vine building algorithms
by: Pfeifer, Dániel, et al.
Published: (2025)

Advanced posterior analyses of hidden Markov models: finite Markov chain imbedding and hybrid decoding
by: Bæk, Zenia Elise Damgaard, et al.
Published: (2025)

Tree-based variational inference for Poisson log-normal models
by: Chaussard, Alexandre, et al.
Published: (2024)

I-trustworthy Models. A framework for trustworthiness evaluation of probabilistic classifiers
by: Vashistha, Ritwik, et al.
Published: (2025)

Extremal graphical modeling with latent variables via convex optimization
by: Engelke, Sebastian, et al.
Published: (2024)