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

278 papers, page 5 of 6

201. NeurIPS 2026Closing the Approximation Gap in Simulation-free Latent SDEs
202. NeurIPS 2026Conformal Prediction for Time-Dependent PDEs
203. NeurIPS 2026Consumer Search and Social Learning in Agentic Markets
204. NeurIPS 2026CoupledFlow: One-Step Neural Operators for Coupled Multi-Physics PDEs
205. NeurIPS 2026Cross-Model Transfer Attacks against Large Vision-Language Models via Model Diversity Enrichment and Stochastic Parameter Sampling
206. NeurIPS 2026Designing Kernel Surrogate Models for Multimodal Attribution
207. NeurIPS 2026DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization
208. NeurIPS 2026Efficient Adaptive Data Acquisition via Pretrained Belief Representations
209. NeurIPS 2026Ensemble Distributionally Robust Bayesian Optimisation
210. NeurIPS 2026Fast Learning Rates for Physics-Informed Kernel Methods
211. NeurIPS 2026Forest-Guided Semantic Transport for Label-Supervised Manifold Alignment
212. NeurIPS 2026From SGD to Muon: Adaptive Optimization via Schatten-p Norms
213. NeurIPS 2026GaitLingo: Self-Supervised Gait Representation Learning with Language Priors
214. NeurIPS 2026GEMS-3D: A Large-Scale 3D Gravity, Electrical, Magnetic, and Seismic Earth Simulation Dataset for Multimodal Geophysical Learning
215. NeurIPS 2026Generalization Error Bounds for Picard-Type Operator Learning in Nonlinear Parabolic PDEs
216. NeurIPS 2026Geometry Meets Physics: Data-Efficient Pre-Training for Unstructured Neural PDE Solvers
217. NeurIPS 2026Geometry-Aware Post-Hoc Uncertainty Quantification in Operator Learning
218. NeurIPS 2026GeoTransolver: Learning Physics on Irregular Domains using Multi-scale Geometry Aware Physics Attention Transformer
219. NeurIPS 2026Goal-Conditioned Supervised Learning for Multi-Objective Recommendation
220. NeurIPS 2026GPA: General Principled Framework for Linearizing Softmax Attention via KV Cache Approximation
221. NeurIPS 2026High-dimensional Limit of SGD for Diagonal Linear Networks
222. NeurIPS 2026How Can SignSGD Outperform SGD? A Functional Scaling Law Perspective
223. NeurIPS 2026Independent Latents, Robust Neural Operators
224. NeurIPS 2026Inpainting physics: self-supervised learning for context-driven fluid simulation
225. NeurIPS 2026Kernel-based guarantees for nonlinear parametric models in Bayesian optimization
226. NeurIPS 2026KerONet: A Single Softmax Readout Suffices for Physics-Informed Operator Learning
227. NeurIPS 2026Latent Debate: A Surrogate Framework for Interpreting LLM Thinking towards Binary Decisions
228. NeurIPS 2026Learning Event-to-Field Operators Without Interpolation
229. NeurIPS 2026Learning to Discriminate Scene Structures Makes Self-Supervised Depth Learning Scalable
230. NeurIPS 2026Less Structure is More: Minimal Representations for Supervised Learning
231. NeurIPS 2026LoRAcles: Self-Supervised Weight-Space Interpretability at Scale
232. NeurIPS 2026Lost or Hidden? A Concept-Level Forgetting in Supervised Continual Learning
233. NeurIPS 2026M$^3$: Reframing Training Measures for Discretized Physical Simulations
234. NeurIPS 2026Machine Learning-Driven RAG System Design
235. NeurIPS 2026Match the Geometry, Skip the Surrogate: Extreme Low-Budget Optimization in High Dimensions
236. NeurIPS 2026Mesh-Free Convolution: Learned Spectral Attenuation and Transport
237. NeurIPS 2026Mitigating Confidence Miscalibration in Open-World Semi-Supervised Learning
238. NeurIPS 2026Multiclass Classification with Rare Useful Features: Fundamental Limits and the Optimality of Diversity Pursuit Higher Criticism
239. NeurIPS 2026Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows
240. NeurIPS 2026Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
241. NeurIPS 2026Neural-Corrected Operator Learning for Homogenization and Inverse Design
242. NeurIPS 2026Newton-PINet: A fast physics-informed neural network with Newton linearization for meta-learning nonlinear PDEs
243. NeurIPS 2026Phaedra: Learning High-Fidelity Discrete Tokenization for the Physical Sciences
244. NeurIPS 2026Post-ADC Inference: Valid Inference After Active Data Collection
245. NeurIPS 2026Pref-DetectGPT: Unveiling Machine-Generated Text via Preference-Aware Curvature Measurement
246. NeurIPS 2026Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement
247. NeurIPS 2026Regret-Based $(\epsilon,\delta)$-optimal Stopping Criteria for Bayesian Optimization
248. NeurIPS 2026Resilient Semi-Supervised Inference with Heterogeneous Unlabeled Data
249. NeurIPS 2026Rethinking Attention in Depth for Operator Learning
250. NeurIPS 2026Robust Surrogate Modeling for Explainable Graph Neural Networks

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.