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← Core Methods
Machine Learning
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Core Methods
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Regression
4,964 papers
Papers per year
2000: 1
2001: 4
2002: 2
2003: 3
2004: 2
2005: 7
2006: 27
2007: 38
2008: 49
2009: 58
2010: 72
2011: 62
2012: 74
2013: 122
2014: 120
2015: 146
2016: 232
2017: 276
2018: 313
2019: 414
2020: 509
2021: 564
2022: 506
2023: 492
2024: 488
2025: 262
2026: 121
Papers
The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response
JMLR 2020
On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent
JMLR 2020
Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables
JMLR 2020
Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory
JMLR 2020
Sparse and low-rank multivariate Hawkes processes
JMLR 2020
Conjugate Gradients for Kernel Machines
JMLR 2020
WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions
JMLR 2020
Quantile Graphical Models: a Bayesian Approach
JMLR 2020
Effective Ways to Build and Evaluate Individual Survival Distributions
JMLR 2020
Distributed Kernel Ridge Regression with Communications
JMLR 2020
Bayesian Model Selection with Graph Structured Sparsity
JMLR 2020
Prediction regions through Inverse Regression
JMLR 2020
Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models
JMLR 2020
Tslearn, A Machine Learning Toolkit for Time Series Data
JMLR 2020
Tensor Regression Networks
JMLR 2020
Distributed Minimum Error Entropy Algorithms
JMLR 2020
A Regularization-Based Adaptive Test for High-Dimensional GLMs
JMLR 2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression
JMLR 2020
Empirical Priors for Prediction in Sparse High-dimensional Linear Regression
JMLR 2020
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
JMLR 2020
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
JMLR 2020
High Dimensional Forecasting via Interpretable Vector Autoregression
JMLR 2020
The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization
JMLR 2020
Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes
JMLR 2020
Optimal Estimation of Sparse Topic Models
JMLR 2020
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