Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasets
cs.LG
Submitted: 2026-09-21
Updated: 2026-09-21
Comments: To appear in The 13th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2026)
Code: https://github.com/boozallen/MOTIF
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Automatic Yara Rule Generation Using Biclustering
- KiloGrams: Very Large N-Grams for Malware Classification
- Intermediate N-Gramming: Deterministic and Fast N-Grams For Large N and Large Datasets
- SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection
- MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels
- A Siren Song of Open Source Reproducibility
- Does the Market of Citations Reward Reproducible Work?
- Research Reproducibility as a Survival Analysis
- A Step Toward Quantifying Independently Reproducible Machine Learning Research
- A Metric Learning Reality Check
- Matrix product ansatz for Fermi fields in one dimension
- Safe Feature Elimination for the LASSO and Sparse Supervised Learning Problems
- A Safe Screening Rule for Sparse Logistic Regression
- Look-Ahead Screening Rules for the Lasso
- The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R
- Benchopt: Reproducible, efficient and collaborative optimization benchmarks
- L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework
- Sparse Online Learning via Truncated Gradient
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks