Latest Research in Machine Learning

Top Research Topics in Machine Learning

• Machine Learning — 18 papers
• Machine Learning — 5 papers
• Statistics Theory — 5 papers
• Methodology — 2 papers
• Machine Learning and Data Classification — 2 papers
• Face and Expression Recognition — 1 papers
• Morphological variations and asymmetry — 1 papers
• Machine Learning in Healthcare — 1 papers
• Gaussian Processes and Bayesian Inference — 1 papers
• Digital Transformation in Industry — 1 papers

Highest-Cited Papers

1. Hierarchical Causal Structure Learning
2. Draining Fictitious Knots: Restoring Distance-Awareness Guarantees for High-Dimensional Spline Networks
3. Intuitionistic Fuzzy Least Square Projection Twin Support Vector Machine for Pattern Classification
4. Split Conformal Prediction with Label-Shift-Adjusted Bayesian Scores
5. EGGROLL, Unrolled: Understanding and Improving Low-Rank Evolution Strategies at Scale
6. Benchmarking non-conformity score functions in conformal prediction
7. Nonparametric Statistics on Stratified Spaces and Their Applications in Object Data Analysis.
8. Learning Multi-Index Models with Hyper-Kernel Ridge Regression
9. DFNN: A Deep Fréchet Neural Network Framework for Learning Metric-Space-Valued Responses
10. Adaptive Conformal Inference Under Delayed Feedback: Coverage Guarantees and a Delay-to-Memory Diagnostic
11. SGD in Multiclass Logistic Regression: Sequential Learning and Scaling Laws
12. Tensor network representations of discrete maximum entropy distributions via mean polytopes
13. Convergent Stochastic Training of Multi-Headed Attention and Understanding LoRA
14. Differentiable Causal Discovery for Singular Linear Models under Confounding
15. Dynamics of Gradient Descent with Large Step Size Near a Manifold of Flat Minima
16. Robust optimization reformulations for support vector machines under label and feature uncertainty
17. On the Asymptotic Inadmissibility of Double Machine Learning Estimators Under Structure-Agnostic Models
18. Inductive Venn-Abers and related regressors
19. Reconciling Universal and Uniform Learning with $Q$-Aggregation
20. Federated Causal Discovery via Regression-Directed Cumulants