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Methodology
← Core Methods
Machine Learning
›
Core Methods
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Regression
4964 directly classified 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
Overparameterized Random Feature Regression with Nearly Orthogonal Data
AISTATS 2023
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning
NIPS 2023
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
NIPS 2023
Equal Opportunity of Coverage in Fair Regression
NIPS 2023
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models
NIPS 2023
Online Linearized LASSO
AISTATS 2023
Demystifying Softmax Gating Function in Gaussian Mixture of Experts
NIPS 2023
Improved Bound on Generalization Error of Compressed KNN Estimator
AISTATS 2023
Robust and Agnostic Learning of Conditional Distributional Treatment Effects
AISTATS 2023
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression
NIPS 2023
Unifying local and global model explanations by functional decomposition of low dimensional structures
AISTATS 2023
From Shapley Values to Generalized Additive Models and back
AISTATS 2023
On Model Selection Consistency of Lasso for High-Dimensional Ising Models
AISTATS 2023
Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions
NIPS 2023
Learning in RKHM: a C*-Algebraic Twist for Kernel Machines
AISTATS 2023
Stochastic Mirror Descent for Large-Scale Sparse Recovery
AISTATS 2023
Frequentist Uncertainty Quantification in Semi-Structured Neural Networks
AISTATS 2023
Is interpolation benign for random forest regression?
AISTATS 2023
Deep Regression Unlearning
ICML 2023
A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization
AISTATS 2023
Learning Neural Volumetric Representations of Dynamic Humans in Minutes
CVPR 2023
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
AISTATS 2023
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis
NIPS 2023
Demographic Parity Constrained Minimax Optimal Regression under Linear Model
NIPS 2023
Coordinate Descent for SLOPE
AISTATS 2023
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