Latest Research in Machine Learning

Top Research Topics in Machine Learning

• Machine Learning — 16 papers
• Methodology — 8 papers
• Machine Learning — 7 papers
• Statistics Theory — 3 papers
• Optimization and Control — 2 papers
• Probabilistic and Robust Engineering Design — 2 papers
• Information Theory — 1 papers
• Computation — 1 papers
• Data Structures and Algorithms — 1 papers
• Statistical Methods and Inference — 1 papers

Highest-Cited Papers

1. Online Adaptive Kernel Mixing for Gaussian Process Decision Making
2. A Bayesian Bi-Directional Splitting Framework for Variable Selection in Large Datasets
3. Double descent is the principle of least action
4. Equivalence Between Nested Gibbs Measures and Log-Linear Combinations of Gibbs Measures
5. Estimation of multiple mean vectors in high dimension
6. Conformal Prediction for Dyadic Regression Under Complex Missingness
7. Local Epochs, Averaging, and Variable Selection in Federated Lasso
8. Efficient Robust Learning at the Information-Theoretic Limit
9. Multicollinearity-agnostic feature screening for non-Euclidean responses: a factor adjusted approach
10. N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
11. Nonsmooth Optimization via Orthogonalized Momentum
12. Selection of tuning parameters in pliable lasso models via modified Bayesian type criteria for high-dimensional data sets
13. L² Sufficiency Theorem under Functional Regime Switching: X-Dependent Convex Combination, Detection Information Loss, Spectral Weighted Sufficiency, and Detection Resolution–Information Lower Bound Duality
14. Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases
15. Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization
16. From Good Starts to Optimal Inference: Generalized Latent Factor Models with Missingness and Implicit Regularization
17. Generalized Score Matching for Parameter Estimation on Convex Domains
18. Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions
19. Formal Bayesian Transfer Learning via the Total Risk Prior
20. A Bayesian Framework for Regularized Estimation in Multivariate Models Integrating Approximate Computing Concepts (1 citations)