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 28 of 29

1351. NeurIPS 2023Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning
1352. NeurIPS 2023Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning
1353. NeurIPS 2023Doubly-Robust Self-Training
1354. NeurIPS 2023DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions
1355. NeurIPS 2023Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes
1356. NeurIPS 2023Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes
1357. NeurIPS 2023Effective Targeted Attacks for Adversarial Self-Supervised Learning
1358. NeurIPS 2023End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
1359. NeurIPS 2023Equal Opportunity of Coverage in Fair Regression
1360. NeurIPS 2023Error Bounds for Learning with Vector-Valued Random Features
1361. NeurIPS 2023Ess-InfoGAIL: Semi-supervised Imitation Learning from Imbalanced Demonstrations
1362. NeurIPS 2023Explaining Predictive Uncertainty with Information Theoretic Shapley Values
1363. NeurIPS 2023Exponentially Convergent Algorithms for Supervised Matrix Factorization
1364. NeurIPS 2023Fair, Polylog-Approximate Low-Cost Hierarchical Clustering
1365. NeurIPS 2023Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
1366. NeurIPS 2023FLSL: Feature-level Self-supervised Learning
1367. NeurIPS 2023Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation
1368. NeurIPS 2023Human-Aligned Calibration for AI-Assisted Decision Making
1369. NeurIPS 2023Identifiable Contrastive Learning with Automatic Feature Importance Discovery
1370. NeurIPS 2023Implicit Contrastive Representation Learning with Guided Stop-gradient
1371. NeurIPS 2023Implicit variance regularization in non-contrastive SSL
1372. NeurIPS 2023Improving Self-supervised Molecular Representation Learning using Persistent Homology
1373. NeurIPS 2023Incentives in Private Collaborative Machine Learning
1374. NeurIPS 2023InstanT: Semi-supervised Learning with Instance-dependent Thresholds
1375. NeurIPS 2023Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation
1376. NeurIPS 2023Koopman Kernel Regression
1377. NeurIPS 2023Label-efficient Segmentation via Affinity Propagation
1378. NeurIPS 2023Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment
1379. NeurIPS 2023Learning From Biased Soft Labels
1380. NeurIPS 2023Learning Interpretable Low-dimensional Representation via Physical Symmetry
1381. NeurIPS 2023Lo-Hi: Practical ML Drug Discovery Benchmark
1382. NeurIPS 2023Low-shot Object Learning with Mutual Exclusivity Bias
1383. NeurIPS 2023Machine learning detects terminal singularities
1384. NeurIPS 2023Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off
1385. NeurIPS 2023Masked Image Residual Learning for Scaling Deeper Vision Transformers
1386. NeurIPS 2023Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
1387. NeurIPS 2023Metis: Understanding and Enhancing In-Network Regular Expressions
1388. NeurIPS 2023Minimum-Risk Recalibration of Classifiers
1389. NeurIPS 2023Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder
1390. NeurIPS 2023Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Federated Object Detection
1391. NeurIPS 2023No Representation Rules Them All in Category Discovery
1392. NeurIPS 2023On Separate Normalization in Self-supervised Transformers
1393. NeurIPS 2023On the Exploitability of Instruction Tuning
1394. NeurIPS 2023On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training
1395. NeurIPS 2023OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
1396. NeurIPS 2023Optimal Block-wise Asymmetric Graph Construction for Graph-based Semi-supervised Learning
1397. NeurIPS 2023Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage
1398. NeurIPS 2023PETAL: Physics Emulation Through Averaged Linearizations for Solving Inverse Problems
1399. NeurIPS 2023Relax, it doesn’t matter how you get there: A new self-supervised approach for multi-timescale behavior analysis
1400. NeurIPS 2023Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective

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.