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

731 papers, page 6 of 15

251. NeurIPS 2025UniMRSeg: Unified Modality-Relax Segmentation via Hierarchical Self-Supervised Compensation
252. NeurIPS 2025Variational Supervised Contrastive Learning
253. NeurIPS 2025Vertical Federated Feature Screening
254. NeurIPS 2025ViSPLA: Visual Iterative Self-Prompting for Language-Guided 3D Affordance Learning
255. NeurIPS 2025What Do Latent Action Models Actually Learn?
256. NeurIPS 2025What Does It Take to Build a Performant Selective Classifier?
257. NeurIPS 2025When No Paths Lead to Rome: Benchmarking Systematic Neural Relational Reasoning
258. NeurIPS 2025WolBanking77: Wolof Banking Speech Intent Classification Dataset
259. NeurIPS 2024(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning
260. NeurIPS 20243D Structure Prediction of Atomic Systems with Flow-based Direct Preference Optimization
261. NeurIPS 2024A Pairwise Pseudo-likelihood Approach for Matrix Completion with Informative Missingness
262. NeurIPS 2024A Structure-Aware Framework for Learning Device Placements on Computation Graphs
263. NeurIPS 2024A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm
264. NeurIPS 2024A theoretical design of concept sets: improving the predictability of concept bottleneck models
265. NeurIPS 2024A Theoretical Perspective for Speculative Decoding Algorithm
266. NeurIPS 2024Accurate and Steady Inertial Pose Estimation through Sequence Structure Learning and Modulation
267. NeurIPS 2024Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
268. NeurIPS 2024Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios
269. NeurIPS 2024Active preference learning for ordering items in- and out-of-sample
270. NeurIPS 2024Active Set Ordering
271. NeurIPS 2024Active, anytime-valid risk controlling prediction sets
272. NeurIPS 2024Ad Auctions for LLMs via Retrieval Augmented Generation
273. NeurIPS 2024AdaNovo: Towards Robust \emph{De Novo} Peptide Sequencing in Proteomics against Data Biases
274. NeurIPS 2024Adaptive Domain Learning for Cross-domain Image Denoising
275. NeurIPS 2024Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation
276. NeurIPS 2024Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization
277. NeurIPS 2024Amortized Active Causal Induction with Deep Reinforcement Learning
278. NeurIPS 2024Amortized Planning with Large-Scale Transformers: A Case Study on Chess
279. NeurIPS 2024An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling
280. NeurIPS 2024An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem
281. NeurIPS 2024Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization
282. NeurIPS 2024Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
283. NeurIPS 2024Approximating mutual information of high-dimensional variables using learned representations
284. NeurIPS 2024Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?
285. NeurIPS 2024Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
286. NeurIPS 2024AUC Maximization under Positive Distribution Shift
287. NeurIPS 2024Autoformalize Mathematical Statements by Symbolic Equivalence and Semantic Consistency
288. NeurIPS 2024AutoManual: Constructing Instruction Manuals by LLM Agents via Interactive Environmental Learning
289. NeurIPS 2024Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records
290. NeurIPS 2024Automatic Outlier Rectification via Optimal Transport
291. NeurIPS 2024Automatically Learning Hybrid Digital Twins of Dynamical Systems
292. NeurIPS 2024BackTime: Backdoor Attacks on Multivariate Time Series Forecasting
293. NeurIPS 2024Batched Energy-Entropy acquisition for Bayesian Optimization
294. NeurIPS 2024Bayesian Adaptive Calibration and Optimal Design
295. NeurIPS 2024Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
296. NeurIPS 2024Better by default: Strong pre-tuned MLPs and boosted trees on tabular data
297. NeurIPS 2024Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization
298. NeurIPS 2024BiScope: AI-generated Text Detection by Checking Memorization of Preceding Tokens
299. NeurIPS 2024Boosted Conformal Prediction Intervals
300. NeurIPS 2024Boosting the Transferability of Adversarial Attack on Vision Transformer with Adaptive Token Tuning

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.