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Methodology
← Core Methods
Machine Learning
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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
Learning Rate Schedules in the Presence of Distribution Shift
ICML 2023
Learning the Efficient Frontier
NIPS 2023
Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency
NIPS 2023
OKRidge: Scalable Optimal k-Sparse Ridge Regression
NIPS 2023
Patch-Level Gaze Distribution Prediction for Gaze Following
WACV 2023
CountNet3D: A 3D Computer Vision Approach To Infer Counts of Occluded Objects
WACV 2023
Multicalibration as Boosting for Regression
ICML 2023
Tensor Gaussian Process with Contraction for Multi-Channel Imaging Analysis
ICML 2023
Smoothed Online Learning for Prediction in Piecewise Affine Systems
NIPS 2023
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability
NIPS 2023
BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series
NIPS 2023
Demographic Parity Constrained Minimax Optimal Regression under Linear Model
NIPS 2023
An Adaptive Algorithm for Learning with Unknown Distribution Drift
NIPS 2023
Greed is good: correspondence recovery for unlabeled linear regression
UAI 2023
Learning Nonlinear Causal Effect via Kernel Anchor Regression
UAI 2023
Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
NIPS 2023
Learning Choice Functions with Gaussian Processes
UAI 2023
Benefits of monotonicity in safe exploration with Gaussian processes
UAI 2023
Robust Gaussian process regression with the trimmed marginal likelihood
UAI 2023
Practical privacy-preserving Gaussian process regression via secret sharing
UAI 2023
Collaboratively Learning Linear Models with Structured Missing Data
NIPS 2023
The past does matter: correlation of subsequent states in trajectory predictions of Gaussian Process models
UAI 2023
A Model-free Closeness-of-influence Test for Features in Supervised Learning
ICML 2023
Saddle-to-Saddle Dynamics in Diagonal Linear Networks
NIPS 2023
Equal Opportunity of Coverage in Fair Regression
NIPS 2023
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