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

98 papers, page 1 of 2

1. NeurIPS 2023A General Framework for Equivariant Neural Networks on Reductive Lie Groups
2. NeurIPS 2023A General Framework for Robust G-Invariance in G-Equivariant Networks
3. NeurIPS 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
4. NeurIPS 2023A Unified Approach to Count-Based Weakly Supervised Learning
5. NeurIPS 2023AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
6. NeurIPS 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
7. NeurIPS 2023An Adaptive Algorithm for Learning with Unknown Distribution Drift
8. NeurIPS 2023Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods
9. NeurIPS 2023Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
10. NeurIPS 2023Binary Classification with Confidence Difference
11. NeurIPS 2023Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders
12. NeurIPS 2023CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning
13. NeurIPS 2023Can semi-supervised learning use all the data effectively? A lower bound perspective
14. NeurIPS 2023CAPro: Webly Supervised Learning with Cross-modality Aligned Prototypes
15. NeurIPS 2023Characterizing the Impacts of Semi-supervised Learning for Weak Supervision
16. NeurIPS 2023Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning
17. NeurIPS 2023ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
18. NeurIPS 2023Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering
19. NeurIPS 2023Combating Representation Learning Disparity with Geometric Harmonization
20. NeurIPS 2023Conditional independence testing under misspecified inductive biases
21. NeurIPS 2023Covariance-adaptive best arm identification
22. NeurIPS 2023Cross-Scale MAE: A Tale of Multiscale Exploitation in Remote Sensing
23. NeurIPS 2023Curriculum Learning With Infant Egocentric Videos
24. NeurIPS 2023DAW: Exploring the Better Weighting Function for Semi-supervised Semantic Segmentation
25. NeurIPS 2023Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning
26. NeurIPS 2023Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning
27. NeurIPS 2023Doubly-Robust Self-Training
28. NeurIPS 2023DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions
29. NeurIPS 2023Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes
30. NeurIPS 2023Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes
31. NeurIPS 2023Effective Targeted Attacks for Adversarial Self-Supervised Learning
32. NeurIPS 2023End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
33. NeurIPS 2023Equal Opportunity of Coverage in Fair Regression
34. NeurIPS 2023Error Bounds for Learning with Vector-Valued Random Features
35. NeurIPS 2023Ess-InfoGAIL: Semi-supervised Imitation Learning from Imbalanced Demonstrations
36. NeurIPS 2023Explaining Predictive Uncertainty with Information Theoretic Shapley Values
37. NeurIPS 2023Exponentially Convergent Algorithms for Supervised Matrix Factorization
38. NeurIPS 2023Fair, Polylog-Approximate Low-Cost Hierarchical Clustering
39. NeurIPS 2023Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
40. NeurIPS 2023FLSL: Feature-level Self-supervised Learning
41. NeurIPS 2023Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation
42. NeurIPS 2023Human-Aligned Calibration for AI-Assisted Decision Making
43. NeurIPS 2023Identifiable Contrastive Learning with Automatic Feature Importance Discovery
44. NeurIPS 2023Implicit Contrastive Representation Learning with Guided Stop-gradient
45. NeurIPS 2023Implicit variance regularization in non-contrastive SSL
46. NeurIPS 2023Improving Self-supervised Molecular Representation Learning using Persistent Homology
47. NeurIPS 2023Incentives in Private Collaborative Machine Learning
48. NeurIPS 2023InstanT: Semi-supervised Learning with Instance-dependent Thresholds
49. NeurIPS 2023Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation
50. NeurIPS 2023Koopman Kernel Regression

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.