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 4 of 29

151. ICML 2026Neural Collapse by Design: Learning Class Prototypes on the Hypersphere
152. ICML 2026Newton-coupled Dual-Teacher Semi-supervised Learning Framework
153. ICML 2026On the Power of Statistics in Class-Incremental Learning with Pretrained Models
154. ICML 2026On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning
155. ICML 2026One Coin Has Two Sides: Single Poistive Multi Label Learning from Salient Annotations
156. ICML 2026Persistent Backdoor Attacks in Class-Incremental Learning via Structural Invariant Anchoring
157. ICML 2026Physics-informed Neural Operator Learning for Nonlinear Grad-Shafranov Equation
158. ICML 2026Pianist Transformer: Towards Expressive Piano Performance Rendering via Scalable Self-Supervised Pre-Training
159. ICML 2026PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalence
160. ICML 2026Quantitative Estimation of Target Task Performance from Unsupervised Pretext Task in Semi/Self-Supervised Learning
161. ICML 2026RECAST: Model Reconstruction via Counterfactual-Aware Wasserstein Geometry under Limited Data
162. ICML 2026Relevance-Based Embeddings: Lightweight Candidate Retrieval via Heavy-Ranker Calls
163. ICML 2026Risk-Bounded Distribution Reconstruction: Stable Statistic Calibration for Long-Tailed Recognition
164. ICML 2026Self-Supervised Learning as Discrete Communication
165. ICML 2026Semi-Supervised Gaze Estimation via Disentangled Subspace Contrastive Learning
166. ICML 2026Semi-Supervised Hypothesis Testing by Betting on Predictions
167. ICML 2026Semi-Supervised Learning for Molecular Graphs via Ensemble Consensus
168. ICML 2026Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression
169. ICML 2026Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain
170. ICML 2026Solving Time-Dependent Differential Equations with Physical Dynamical Systems
171. ICML 2026SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport
172. ICML 2026SpikeCLR: Self-Supervised Contrastive Learning for Visual Representations with Spiking Neural Networks
173. ICML 2026STAND: Self-Aware Precondition Induction for Interactive Task Learning
174. ICML 2026Stochastic Lifting for Generating Trajectories of Stochastic Physical Systems
175. ICML 2026TabularBERT: Binning-Based Self-Supervised Learning for Tabular Representation
176. ICML 2026Taking the GP Out of the Loop
177. ICML 2026The Extra Tokens Matter: Disentangled Representation Learning with Vision Transformers
178. ICML 2026Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exit
179. ICML 2026TN-SHAP-G: Graph-Structured Tensor Network Surrogates for Shapley Values and Interactions
180. ICML 2026Topology-Preserving Neural Operator Learning via Hodge Decomposition
181. ICML 2026Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression
182. ICML 2026Trajectory-Aware Certified Decentralized Unlearning via SGD Stability
183. ICML 2026Transformer Circuits Can Realize Clustering Algorithms
184. ICML 2026Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries
185. ICML 2026Trust Functions: Near Lossless Weak-to-Strong Generalization by Learning to Trust the Weak Teacher
186. ICML 2026Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
187. ICML 2026Tuning-Free One-Class Discriminant Learning for Tabular Anomaly Detection
188. ICML 2026Two-Layer Linear Auto-Regressive Models Estimate Latent States
189. ICML 2026Uncovering the Gradient Geometry of Long CoT: A Spectral-guided Approach to Reasoning Distillation
190. ICML 2026Understanding Self-Supervised Learning via Latent Distribution Matching
191. ICML 2026When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer
192. NeurIPS 2026AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training
193. NeurIPS 2026Approximation of Maximally Monotone Operators : A Graph Convergence Perspective
194. NeurIPS 2026Are Well-Trained Surrogates Optimal? Rethinking the Surrogate Role with Instability for Unlearnable Examples
195. NeurIPS 2026AudioSphere: Towards Self-Supervised Spatial Audio Representation Models
196. NeurIPS 2026Auditing Single-Query Recoverability in Self-Supervised Representations
197. NeurIPS 2026BayesAT: Bayes-Guided Progressive Distillation for Semi-Supervised Adversarial Training
198. NeurIPS 2026Capturing Membrane Dynamics and Spike Timing of Human Neurons at Scale using Neural Operators
199. NeurIPS 2026Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs
200. NeurIPS 2026CLEAR: Complementary Tripartite Play with Bayesian Calibration for Semi-Supervised Edge Classification

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.