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

1,424 papers, page 24 of 29

1151. NeurIPS 2024Verified Code Transpilation with LLMs
1152. NeurIPS 2024VISA: Variational Inference with Sequential Sample-Average Approximations
1153. NeurIPS 2024Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
1154. NeurIPS 2024Weight Diffusion for Future: Learn to Generalize in Non-Stationary Environments
1155. NeurIPS 2024What Makes Partial-Label Learning Algorithms Effective?
1156. NeurIPS 2024What makes unlearning hard and what to do about it
1157. NeurIPS 2024When Is Inductive Inference Possible?
1158. NeurIPS 2024Why Go Full? Elevating Federated Learning Through Partial Network Updates
1159. NeurIPS 2024WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics
1160. NeurIPS 2024You Don’t Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning
1161. NeurIPS 2024ZOPP: A Framework of Zero-shot Offboard Panoptic Perception for Autonomous Driving
1162. CVPR 2023A General Regret Bound of Preconditioned Gradient Method for DNN Training
1163. CVPR 2023AstroNet: When Astrocyte Meets Artificial Neural Network
1164. CVPR 2023AttentionShift: Iteratively Estimated Part-Based Attention Map for Pointly Supervised Instance Segmentation
1165. CVPR 2023Augmentation Matters: A Simple-Yet-Effective Approach to Semi-Supervised Semantic Segmentation
1166. CVPR 2023Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
1167. CVPR 2023Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
1168. CVPR 2023Boundary-Enhanced Co-Training for Weakly Supervised Semantic Segmentation
1169. CVPR 2023BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation
1170. CVPR 2023Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural Fields
1171. CVPR 2023Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning
1172. CVPR 2023Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation
1173. CVPR 2023Consistent-Teacher: Towards Reducing Inconsistent Pseudo-Targets in Semi-Supervised Object Detection
1174. CVPR 2023Coreset Sampling From Open-Set for Fine-Grained Self-Supervised Learning
1175. CVPR 2023Deep Semi-Supervised Metric Learning With Mixed Label Propagation
1176. CVPR 2023DLBD: A Self-Supervised Direct-Learned Binary Descriptor
1177. CVPR 2023DualRel: Semi-Supervised Mitochondria Segmentation From a Prototype Perspective
1178. CVPR 2023Ensemble-Based Blackbox Attacks on Dense Prediction
1179. CVPR 2023Evolved Part Masking for Self-Supervised Learning
1180. CVPR 2023Fully Self-Supervised Depth Estimation From Defocus Clue
1181. CVPR 2023Fuzzy Positive Learning for Semi-Supervised Semantic Segmentation
1182. CVPR 2023GeneCIS: A Benchmark for General Conditional Image Similarity
1183. CVPR 2023HyperMatch: Noise-Tolerant Semi-Supervised Learning via Relaxed Contrastive Constraint
1184. CVPR 2023Improving Weakly Supervised Temporal Action Localization by Bridging Train-Test Gap in Pseudo Labels
1185. CVPR 2023Learning Common Rationale To Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems
1186. CVPR 2023Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking
1187. CVPR 2023MAESTER: Masked Autoencoder Guided Segmentation at Pixel Resolution for Accurate, Self-Supervised Subcellular Structure Recognition
1188. CVPR 2023MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins
1189. CVPR 2023MixTeacher: Mining Promising Labels With Mixed Scale Teacher for Semi-Supervised Object Detection
1190. CVPR 2023Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
1191. CVPR 2023NeRF-Supervised Deep Stereo
1192. CVPR 2023Optimal Transport Minimization: Crowd Localization on Density Maps for Semi-Supervised Counting
1193. CVPR 2023Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation
1194. CVPR 2023Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning
1195. CVPR 2023PivoTAL: Prior-Driven Supervision for Weakly-Supervised Temporal Action Localization
1196. CVPR 2023PlaneDepth: Self-Supervised Depth Estimation via Orthogonal Planes
1197. CVPR 2023PointCMP: Contrastive Mask Prediction for Self-Supervised Learning on Point Cloud Videos
1198. CVPR 2023Pose-Disentangled Contrastive Learning for Self-Supervised Facial Representation
1199. CVPR 2023Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal Action Localization
1200. CVPR 2023RefCLIP: A Universal Teacher for Weakly Supervised Referring Expression Comprehension

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.