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

171 papers, page 1 of 4

1. NeurIPS 2025$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning
2. NeurIPS 2025$\epsilon$-Seg: Sparsely Supervised Semantic Segmentation of Microscopy Data
3. NeurIPS 20251000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
4. NeurIPS 20253DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimization
5. NeurIPS 202570% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float (DFloat11)
6. NeurIPS 2025A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging
7. NeurIPS 2025Accelerating Model-Free Optimization via Averaging of Cost Samples
8. NeurIPS 2025AdaTS: Learning Adaptive Time Series Representations via Dynamic Soft Contrasts
9. NeurIPS 2025Advancing Interpretability of CLIP Representations with Concept Surrogate Model
10. NeurIPS 2025Adversarial Graph Fusion for Incomplete Multi-view Semi-supervised Learning with Tensorial Imputation
11. NeurIPS 2025AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation
12. NeurIPS 2025An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph
13. NeurIPS 2025An Information-theoretical Framework for Understanding Out-of-distribution Detection with Pretrained Vision-Language Models
14. NeurIPS 2025Analog Foundation Models
15. NeurIPS 2025Anchor-based Maximum Discrepancy for Relative Similarity Testing
16. NeurIPS 2025Anytime-valid, Bayes-assisted, Prediction-Powered Inference
17. NeurIPS 2025ArchPower: Dataset for Architecture-Level Power Modeling of Modern CPU Design
18. NeurIPS 2025Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning
19. NeurIPS 2025Asymptotically Stable Quaternion-valued Hopfield-structured Neural Network with Periodic Projection-based Supervised Learning Rules
20. NeurIPS 2025AutoHood3D: A Multi‑Modal Benchmark for Automotive Hood Design and Fluid–Structure Interaction
21. NeurIPS 2025Backdoor Mitigation via Invertible Pruning Masks
22. NeurIPS 2025Bayesian Optimization with Preference Exploration using a Monotonic Neural Network Ensemble
23. NeurIPS 2025Boosting Adversarial Transferability with Spatial Adversarial Alignment
24. NeurIPS 2025Breakthrough Sensor-Limited Single View: Towards Implicit Temporal Dynamics for Time Series Domain Adaptation
25. NeurIPS 2025CALM-PDE: Continuous and Adaptive Convolutions for Latent Space Modeling of Time-dependent PDEs
26. NeurIPS 2025CLEAR: Command Level Annotated Dataset for Ransomware Detection
27. NeurIPS 2025CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
28. NeurIPS 2025Coloring Learning for Heterophilic Graph Representation
29. NeurIPS 2025ComRank: Ranking Loss for Multi-Label Complementary Label Learning
30. NeurIPS 2025Confusion-Driven Self-Supervised Progressively Weighted Ensemble Learning for Non-Exemplar Class Incremental Learning
31. NeurIPS 2025Consensus-Robust Transfer Attacks via Parameter and Representation Perturbations
32. NeurIPS 2025Contrastive Self-Supervised Learning As Neural Manifold Packing
33. NeurIPS 2025Correcting misinterpretations of additive models
34. NeurIPS 2025Cost-Sensitive Freeze-thaw Bayesian Optimization for Efficient Hyperparameter Tuning
35. NeurIPS 2025CoUn: Empowering Machine Unlearning via Contrastive Learning
36. NeurIPS 2025Data-Free Model Extraction for Black-box Recommender Systems via Graph Convolutions
37. NeurIPS 2025Decompile-Bench: Million-Scale Binary-Source Function Pairs for Real-World Binary Decompilation
38. NeurIPS 2025DecompNet: Enhancing Time Series Forecasting Models with Implicit Decomposition
39. NeurIPS 2025Decoupled Entropy Minimization
40. NeurIPS 2025DeltaPhi: Physical States Residual Learning for Neural Operators in Data-Limited PDE Solving
41. NeurIPS 2025Demystifying Spectral Feature Learning for Instrumental Variable Regression
42. NeurIPS 2025Diffusion-Guided Graph Data Augmentation
43. NeurIPS 2025Disentanglement Beyond Static vs. Dynamic: A Benchmark and Evaluation Framework for Multi-Factor Sequential Representations
44. NeurIPS 2025Distributionally Robust Performative Optimization
45. NeurIPS 2025Ditch the Denoiser: Emergence of Noise Robustness in Self-Supervised Learning from Data Curriculum
46. NeurIPS 2025Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
47. NeurIPS 2025DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
48. NeurIPS 2025DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing
49. NeurIPS 2025EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale
50. NeurIPS 2025Efficient Knowledge Transfer in Federated Recommendation for Joint Venture Ecosystem

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.