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

114 papers, page 1 of 3

1. ICML 2025A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning
2. ICML 2025Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$
3. ICML 2025Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators
4. ICML 2025Active Learning with Selective Time-Step Acquisition for PDEs
5. ICML 2025Adaptive Partitioning Schemes for Optimistic Optimization
6. ICML 2025Almost Optimal Fully Dynamic $k$-Center Clustering with Recourse
7. ICML 2025An Analysis of Quantile Temporal-Difference Learning
8. ICML 2025Approximately Correct Label Distribution Learning
9. ICML 2025Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation
10. ICML 2025Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data
11. ICML 2025Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
12. ICML 2025BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach
13. ICML 2025Calibrated Physics-Informed Uncertainty Quantification
14. ICML 2025Clustering Properties of Self-Supervised Learning
15. ICML 2025Clustering via Self-Supervised Diffusion
16. ICML 2025CoastalBench: A Decade-Long High-Resolution Dataset to Emulate Complex Coastal Processes
17. ICML 2025Collapse-Proof Non-Contrastive Self-Supervised Learning
18. ICML 2025Compressing tree ensembles through Level-wise Optimization and Pruning
19. ICML 2025Concentration Distribution Learning from Label Distributions
20. ICML 2025Confidence Difference Reflects Various Supervised Signals in Confidence-Difference Classification
21. ICML 2025Correlation Clustering Beyond the Pivot Algorithm
22. ICML 2025Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
23. ICML 2025Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models
24. ICML 2025Deep Electromagnetic Structure Design Under Limited Evaluation Budgets
25. ICML 2025DeepLayout: Learning Neural Representations of Circuit Placement Layout
26. ICML 2025Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning
27. ICML 2025Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization
28. ICML 2025Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning
29. ICML 2025Disentangling Invariant Subgraph via Variance Contrastive Estimation under Distribution Shifts
30. ICML 2025Divide and Conquer: Learning Label Distribution with Subtasks
31. ICML 2025Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning
32. ICML 2025Enhancing Logits Distillation with Plug&Play Kendall's $\tau$ Ranking Loss
33. ICML 2025Enhancing Target-unspecific Tasks through a Features Matrix
34. ICML 2025EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification
35. ICML 2025ENSUR: Equitable and Statistically Unbiased Recommendation
36. ICML 2025Equivalence is All: A Unified View for Self-supervised Graph Learning
37. ICML 2025FedClean: A General Robust Label Noise Correction for Federated Learning
38. ICML 2025FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain
39. ICML 2025Focal-SAM: Focal Sharpness-Aware Minimization for Long-Tailed Classification
40. ICML 2025From Logits to Hierarchies: Hierarchical Clustering made Simple
41. ICML 2025Generalized additive models via direct optimization of regularized decision stump forests
42. ICML 2025Generalized Random Forests Using Fixed-Point Trees
43. ICML 2025Generative Modeling Reinvents Supervised Learning: Label Repurposing with Predictive Consistency Learning
44. ICML 2025Gradient Aligned Regression via Pairwise Losses
45. ICML 2025HEAP: Hyper Extended A-PDHG Operator for Constrained High-dim PDEs
46. ICML 2025HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport
47. ICML 2025Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space
48. ICML 2025Human Cognition-Inspired Hierarchical Fuzzy Learning Machine
49. ICML 2025Hyperband-based Bayesian Optimization for Black-box Prompt Selection
50. ICML 2025Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent

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.