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

375 papers, page 4 of 8

151. NeurIPS 2024HOPE: Shape Matching Via Aligning Different K-hop Neighbourhoods
152. NeurIPS 2024HORSE: Hierarchical Representation for Large-Scale Neural Subset Selection
153. NeurIPS 2024How Does Message Passing Improve Collaborative Filtering?
154. NeurIPS 2024How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval
155. NeurIPS 2024Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability
156. NeurIPS 2024HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning
157. NeurIPS 2024Hyperbolic Embeddings of Supervised Models
158. NeurIPS 2024HYSYNTH: Context-Free LLM Approximation for Guiding Program Synthesis
159. NeurIPS 2024Identifiable Shared Component Analysis of Unpaired Multimodal Mixtures
160. NeurIPS 2024Identify Then Recommend: Towards Unsupervised Group Recommendation
161. NeurIPS 2024Identifying Equivalent Training Dynamics
162. NeurIPS 2024ImageNet3D: Towards General-Purpose Object-Level 3D Understanding
163. NeurIPS 2024Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient
164. NeurIPS 2024Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach
165. NeurIPS 2024Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques
166. NeurIPS 2024Induced Model Matching: Restricted Models Help Train Full-Featured Models
167. NeurIPS 2024Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference
168. NeurIPS 2024Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models
169. NeurIPS 2024Initializing Services in Interactive ML Systems for Diverse Users
170. NeurIPS 2024Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation
171. NeurIPS 2024Interventionally Consistent Surrogates for Complex Simulation Models
172. NeurIPS 2024Inversion-based Latent Bayesian Optimization
173. NeurIPS 2024IWBVT: Instance Weighting-based Bias-Variance Trade-off for Crowdsourcing
174. NeurIPS 2024KFNN: K-Free Nearest Neighbor For Crowdsourcing
175. NeurIPS 2024KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization
176. NeurIPS 2024Large Pre-trained time series models for cross-domain Time series analysis tasks
177. NeurIPS 2024Large Scale Transfer Learning for Tabular Data via Language Modeling
178. NeurIPS 2024Layer-Adaptive State Pruning for Deep State Space Models
179. NeurIPS 2024Learnability Matters: Active Learning for Video Captioning
180. NeurIPS 2024Learning Better Representations From Less Data For Propositional Satisfiability
181. NeurIPS 2024Learning Complete Protein Representation by Dynamically Coupling of Sequence and Structure
182. NeurIPS 2024Learning Formal Mathematics From Intrinsic Motivation
183. NeurIPS 2024Learning from Uncertain Data: From Possible Worlds to Possible Models
184. NeurIPS 2024Learning Plaintext-Ciphertext Cryptographic Problems via ANF-based SAT Instance Representation
185. NeurIPS 2024Learning Representations for Hierarchies with Minimal Support
186. NeurIPS 2024Learning Structure-Aware Representations of Dependent Types
187. NeurIPS 2024Learning Structured Representations with Hyperbolic Embeddings
188. NeurIPS 2024Learning the Infinitesimal Generator of Stochastic Diffusion Processes
189. NeurIPS 2024Learning to compute Gröbner bases
190. NeurIPS 2024Learning to Embed Distributions via Maximum Kernel Entropy
191. NeurIPS 2024Learning to Predict Structural Vibrations
192. NeurIPS 2024Learning Where to Edit Vision Transformers
193. NeurIPS 2024Learning-Augmented Priority Queues
194. NeurIPS 2024LLaMo: Large Language Model-based Molecular Graph Assistant
195. NeurIPS 2024LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation
196. NeurIPS 2024LLMDFA: Analyzing Dataflow in Code with Large Language Models
197. NeurIPS 2024Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
198. NeurIPS 2024LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment
199. NeurIPS 2024Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling
200. NeurIPS 2024MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution

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.