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 2024A General Framework for Learning from Weak Supervision
2. ICML 2024A New Branch-and-Bound Pruning Framework for $\ell_0$-Regularized Problems
3. ICML 2024A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
4. ICML 2024A3S: A General Active Clustering Method with Pairwise Constraints
5. ICML 2024Be Your Own Neighborhood: Detecting Adversarial Examples by the Neighborhood Relations Built on Self-Supervised Learning
6. ICML 2024Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning
7. ICML 2024Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains
8. ICML 2024Boosting Offline Optimizers with Surrogate Sensitivity
9. ICML 2024Bootstrap AutoEncoders With Contrastive Paradigm for Self-supervised Gaze Estimation
10. ICML 2024Compress Clean Signal from Noisy Raw Image: A Self-Supervised Approach
11. ICML 2024Concentration Inequalities for General Functions of Heavy-Tailed Random Variables
12. ICML 2024ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy
13. ICML 2024Correlation-Induced Label Prior for Semi-Supervised Multi-Label Learning
14. ICML 2024Counterfactual Metarules for Local and Global Recourse
15. ICML 2024Deep Stochastic Mechanics
16. ICML 2024Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior
17. ICML 2024Enhancing Trajectory Prediction through Self-Supervised Waypoint Distortion Prediction
18. ICML 2024Equivariant Graph Neural Operator for Modeling 3D Dynamics
19. ICML 2024Exploring Correlations of Self-Supervised Tasks for Graphs
20. ICML 2024FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data
21. ICML 2024From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble
22. ICML 2024Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution
23. ICML 2024High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
24. ICML 2024Implicit Representations via Operator Learning
25. ICML 2024Improved Operator Learning by Orthogonal Attention
26. ICML 2024In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
27. ICML 2024Information Flow in Self-Supervised Learning
28. ICML 2024Learning Surrogates for Offline Black-Box Optimization via Gradient Matching
29. ICML 2024MALIBO: Meta-learning for Likelihood-free Bayesian Optimization
30. ICML 2024Neural Operators with Localized Integral and Differential Kernels
31. ICML 2024OTMatch: Improving Semi-Supervised Learning with Optimal Transport
32. ICML 2024OxyGenerator: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning
33. ICML 2024Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning
34. ICML 2024Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning
35. ICML 2024Scribble-Supervised Semantic Segmentation with Prototype-based Feature Augmentation
36. ICML 2024Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation
37. ICML 2024Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity
38. ICML 2024SSL4Q: Semi-Supervised Learning of Quantum Data with Application to Quantum State Classification
39. ICML 2024Towards Realistic Model Selection for Semi-supervised Learning
40. ICML 2024Transferable Facial Privacy Protection against Blind Face Restoration via Domain-Consistent Adversarial Obfuscation
41. ICML 2024Unveiling the Dynamics of Information Interplay in Supervised Learning
42. ICML 2024UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis
43. ICML 2024Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations
44. ICML 2024Weakly-Supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimation

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.