General ML · supervised learning papers, 2023–2026

Key findings

• General ML · supervised learning went from 263 papers in 2023 to 279 in 2026, roughly 1.1x in three years.
• NeurIPS publishes the most General ML · supervised learning work: 731 papers from 2023 to 2026, 51% of the four-venue total.
• The fastest-growing overlap is with Diffusion Models: papers tagged with both rose from 2 in 2023 to 9 in 2026.
• Among the 6 topics under General Machine Learning, it ranks #3 by 2026 paper count; the fastest grower in the group, General ML · evaluation, is up 4.3x versus 1.1x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training
• ICML 2026$\mathbb{R}^{2k}$ is Theoretically Large Enough for Embedding-based Top-$k$ Retrieval
• ICLR 2026Adaptive Concept Discovery for Interpretable Few-Shot Text Classification
• CVPR 2026Enabling Supervised Learning of Generative Signatures for Generalized AI-Generated Images
• NeurIPS 2025$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning
• ICML 2025Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$
• ICLR 2025Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations
• CVPR 2025A Unified Framework for Heterogeneous Semi-supervised Learning
• NeurIPS 2024(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning
• ICML 2024A New Branch-and-Bound Pruning Framework for $\ell_0$-Regularized Problems
• ICLR 2024A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
• CVPR 2024Efficient Hyperparameter Optimization with Adaptive Fidelity Identification
• NeurIPS 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
• ICML 2023A Model-free Closeness-of-influence Test for Features in Supervised Learning
• ICLR 2023Deep Ranking Ensembles for Hyperparameter Optimization
• CVPR 2023Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
• NeurIPS 2026Approximation of Maximally Monotone Operators : A Graph Convergence Perspective
• ICML 2026Active Budget Allocation for Efficient Scaling Law Estimation via Surrogate-Guided Pruning
• ICLR 2026Buckingham $\pi$-Invariant Test‑Time Projection for Robust PDE Surrogate Modeling
• CVPR 2026HyperNAS Enhancing Architecture Representation for NAS Predictor via Hypernetwork

All General ML · supervised learning papers

114 papers, page 2 of 3

51. ICML 2025Improving Multi-Class Calibration through Normalization-Aware Isotonic Techniques
52. ICML 2025Instance Correlation Graph-based Naive Bayes
53. ICML 2025Label Distribution Propagation-based Label Completion for Crowdsourcing
54. ICML 2025LADA: Scalable Label-Specific CLIP Adapter for Continual Learning
55. ICML 2025Learning Compact Semantic Information for Incomplete Multi-View Missing Multi-Label Classification
56. ICML 2025Learning from True-False Labels via Multi-modal Prompt Retrieving
57. ICML 2025Learning Imbalanced Data with Beneficial Label Noise
58. ICML 2025Learning Monotonic Probabilities with a Generative Cost Model
59. ICML 2025Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
60. ICML 2025M2PDE: Compositional Generative Multiphysics and Multi-component PDE Simulation
61. ICML 2025MetricEmbedding: Accelerate Metric Nearness by Tropical Inner Product
62. ICML 2025MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
63. ICML 2025Modified K-means Algorithm with Local Optimality Guarantees
64. ICML 2025MOGIC: Metadata-infused Oracle Guidance for Improved Extreme Classification
65. ICML 2025Multivariate Conformal Selection
66. ICML 2025Non-Stationary Predictions May Be More Informative: Exploring Pseudo-Labels with a Two-Phase Pattern of Training Dynamics
67. ICML 2025Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks
68. ICML 2025On the Benefits of Active Data Collection in Operator Learning
69. ICML 2025On the Importance of Embedding Norms in Self-Supervised Learning
70. ICML 2025On the Out-of-Distribution Generalization of Self-Supervised Learning
71. ICML 2025Optimization for Neural Operators can Benefit from Width
72. ICML 2025Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
73. ICML 2025Pareto-Optimal Fronts for Benchmarking Symbolic Regression Algorithms
74. ICML 2025Position: A Critical Perspective on The Value in Studying Deep Learning Phenomena
75. ICML 2025Position: Supervised Classifiers Answer the Wrong Questions for OOD Detection
76. ICML 2025Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards
77. ICML 2025Prediction models that learn to avoid missing values
78. ICML 2025Predictive Performance of Deep Quantum Data Re-uploading Models
79. ICML 2025Preserving AUC Fairness in Learning with Noisy Protected Groups
80. ICML 2025Principled Algorithms for Optimizing Generalized Metrics in Binary Classification
81. ICML 2025Provably Efficient RL for Linear MDPs under Instantaneous Safety Constraints in Non-Convex Feature Spaces
82. ICML 2025Quantum Optimization via Gradient-Based Hamiltonian Descent
83. ICML 2025Random Feature Representation Boosting
84. ICML 2025Rank-One Modified Value Iteration
85. ICML 2025Rethinking Confidence Scores and Thresholds in Pseudolabeling-based SSL
86. ICML 2025Right Time to Learn: Promoting Generalization via Bio-inspired Spacing Effect in Knowledge Distillation
87. ICML 2025Robust Automatic Modulation Classification with Fuzzy Regularization
88. ICML 2025Safety Certificate against Latent Variables with Partially Unidentifiable Dynamics
89. ICML 2025scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
90. ICML 2025SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations
91. ICML 2025SDMG: Smoothing Your Diffusion Models for Powerful Graph Representation Learning
92. ICML 2025Self-supervised Masked Graph Autoencoder via Structure-aware Curriculum
93. ICML 2025Self-Supervised Transformers as Iterative Solution Improvers for Constraint Satisfaction
94. ICML 2025Shifting Time: Time-series Forecasting with Khatri-Rao Neural Operators
95. ICML 2025Simplifying DINO via Coding Rate Regularization
96. ICML 2025Spherical Rotation Dimension Reduction with Geometric Loss Functions
97. ICML 2025Stabilizing Sample Similarity in Representation via Mitigating Random Consistency
98. ICML 2025Stray Intrusive Outliers-Based Feature Selection on Intra-Class Asymmetric Instance Distribution or Multiple High-Density Clusters
99. ICML 2025Survival Analysis via Density Estimation
100. ICML 2025Tensorized Multi-View Multi-Label Classification via Laplace Tensor Rank

FAQ

How many General ML · supervised learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,425 in total: 263 in 2023, 511 in 2024, 372 in 2025, 279 in 2026.

How many General ML · supervised learning papers are at NeurIPS 2026?

NeurIPS 2026 has 87 General ML · supervised learning papers, versus 171 the year before.

How many General ML · supervised learning papers are at ICLR 2026?

ICLR 2026 has 48 General ML · supervised learning papers, versus 40 the year before.

How many General ML · supervised learning papers are at ICML 2026?

ICML 2026 has 111 General ML · supervised learning papers, versus 114 the year before.

How many General ML · supervised learning papers are at CVPR 2026?

CVPR 2026 has 33 General ML · supervised learning papers, versus 47 the year before.

Which conference publishes the most General ML · supervised learning papers?

NeurIPS, with 731 papers from 2023 to 2026.