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

263 papers, page 4 of 6

151. ICML 2023RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank
152. ICML 2023Sample Complexity of Probability Divergences under Group Symmetry
153. ICML 2023Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable
154. ICML 2023Self-supervised learning of Split Invariant Equivariant representations
155. ICML 2023Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
156. ICML 2023Short-lived High-volume Bandits
157. ICML 2023Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
158. ICML 2023Statistical Inference and A/B Testing for First-Price Pacing Equilibria
159. ICML 2023Towards Constituting Mathematical Structures for Learning to Optimize
160. ICML 2023Towards Quantum Machine Learning for Constrained Combinatorial Optimization: a Quantum QAP Solver
161. ICML 2023Training Deep Surrogate Models with Large Scale Online Learning
162. ICML 2023Underspecification Presents Challenges for Credibility in Modern Machine Learning
163. ICML 2023Variational Autoencoding Neural Operators
164. ICML 2023Weakly Supervised Regression with Interval Targets
165. ICML 2023Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?
166. NeurIPS 2023A General Framework for Equivariant Neural Networks on Reductive Lie Groups
167. NeurIPS 2023A General Framework for Robust G-Invariance in G-Equivariant Networks
168. NeurIPS 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
169. NeurIPS 2023A Unified Approach to Count-Based Weakly Supervised Learning
170. NeurIPS 2023AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
171. NeurIPS 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
172. NeurIPS 2023An Adaptive Algorithm for Learning with Unknown Distribution Drift
173. NeurIPS 2023Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods
174. NeurIPS 2023Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
175. NeurIPS 2023Binary Classification with Confidence Difference
176. NeurIPS 2023Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders
177. NeurIPS 2023CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning
178. NeurIPS 2023Can semi-supervised learning use all the data effectively? A lower bound perspective
179. NeurIPS 2023CAPro: Webly Supervised Learning with Cross-modality Aligned Prototypes
180. NeurIPS 2023Characterizing the Impacts of Semi-supervised Learning for Weak Supervision
181. NeurIPS 2023Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning
182. NeurIPS 2023ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
183. NeurIPS 2023Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering
184. NeurIPS 2023Combating Representation Learning Disparity with Geometric Harmonization
185. NeurIPS 2023Conditional independence testing under misspecified inductive biases
186. NeurIPS 2023Covariance-adaptive best arm identification
187. NeurIPS 2023Cross-Scale MAE: A Tale of Multiscale Exploitation in Remote Sensing
188. NeurIPS 2023Curriculum Learning With Infant Egocentric Videos
189. NeurIPS 2023DAW: Exploring the Better Weighting Function for Semi-supervised Semantic Segmentation
190. NeurIPS 2023Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning
191. NeurIPS 2023Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning
192. NeurIPS 2023Doubly-Robust Self-Training
193. NeurIPS 2023DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions
194. NeurIPS 2023Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes
195. NeurIPS 2023Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes
196. NeurIPS 2023Effective Targeted Attacks for Adversarial Self-Supervised Learning
197. NeurIPS 2023End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
198. NeurIPS 2023Equal Opportunity of Coverage in Fair Regression
199. NeurIPS 2023Error Bounds for Learning with Vector-Valued Random Features
200. NeurIPS 2023Ess-InfoGAIL: Semi-supervised Imitation Learning from Imbalanced Demonstrations

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.