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

44 papers, page 1 of 1

1. ICML 2023A Model-free Closeness-of-influence Test for Features in Supervised Learning
2. ICML 2023A Neural PDE Solver with Temporal Stencil Modeling
3. ICML 2023Active Ranking of Experts Based on their Performances in Many Tasks
4. ICML 2023Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano
5. ICML 2023AutoCoreset: An Automatic Practical Coreset Construction Framework
6. ICML 2023Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions
7. ICML 2023Continual Learners are Incremental Model Generalizers
8. ICML 2023CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
9. ICML 2023Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least
10. ICML 2023Dimensionality Reduction for General KDE Mode Finding
11. ICML 2023Disentangled Multi-Fidelity Deep Bayesian Active Learning
12. ICML 2023Distance Weighted Supervised Learning for Offline Interaction Data
13. ICML 2023Evaluating Self-Supervised Learning via Risk Decomposition
14. ICML 2023Fast Sampling of Diffusion Models via Operator Learning
15. ICML 2023Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods
16. ICML 2023GNOT: A General Neural Operator Transformer for Operator Learning
17. ICML 2023Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression
18. ICML 2023Learning Neural PDE Solvers with Parameter-Guided Channel Attention
19. ICML 2023Learning the Right Layers a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs
20. ICML 2023Loss Balancing for Fair Supervised Learning
21. ICML 2023Machine Learning Force Fields with Data Cost Aware Training
22. ICML 2023Minimizing Trajectory Curvature of ODE-based Generative Models
23. ICML 2023Neural Inverse Operators for Solving PDE Inverse Problems
24. ICML 2023Neural Status Registers
25. ICML 2023Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization
26. ICML 2023NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data
27. ICML 2023On the Stepwise Nature of Self-Supervised Learning
28. ICML 2023Optimizing Hyperparameters with Conformal Quantile Regression
29. ICML 2023Random Teachers are Good Teachers
30. ICML 2023RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank
31. ICML 2023Sample Complexity of Probability Divergences under Group Symmetry
32. ICML 2023Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable
33. ICML 2023Self-supervised learning of Split Invariant Equivariant representations
34. ICML 2023Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
35. ICML 2023Short-lived High-volume Bandits
36. ICML 2023Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
37. ICML 2023Statistical Inference and A/B Testing for First-Price Pacing Equilibria
38. ICML 2023Towards Constituting Mathematical Structures for Learning to Optimize
39. ICML 2023Towards Quantum Machine Learning for Constrained Combinatorial Optimization: a Quantum QAP Solver
40. ICML 2023Training Deep Surrogate Models with Large Scale Online Learning
41. ICML 2023Underspecification Presents Challenges for Credibility in Modern Machine Learning
42. ICML 2023Variational Autoencoding Neural Operators
43. ICML 2023Weakly Supervised Regression with Interval Targets
44. ICML 2023Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?

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.